Overview

Dataset statistics

Number of variables39
Number of observations118315
Missing cells182649
Missing cells (%)4.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory208.8 MiB
Average record size in memory1.8 KiB

Variable types

Categorical24
Numeric15

Warnings

order_id has a high cardinality: 98665 distinct values High cardinality
product_id has a high cardinality: 32951 distinct values High cardinality
seller_id has a high cardinality: 3095 distinct values High cardinality
shipping_limit_date has a high cardinality: 93317 distinct values High cardinality
product_category_name has a high cardinality: 73 distinct values High cardinality
seller_city has a high cardinality: 611 distinct values High cardinality
review_id has a high cardinality: 98452 distinct values High cardinality
review_comment_title has a high cardinality: 4569 distinct values High cardinality
review_comment_message has a high cardinality: 36434 distinct values High cardinality
review_creation_date has a high cardinality: 633 distinct values High cardinality
review_answer_timestamp has a high cardinality: 98290 distinct values High cardinality
customer_id has a high cardinality: 98665 distinct values High cardinality
order_purchase_timestamp has a high cardinality: 98111 distinct values High cardinality
order_approved_at has a high cardinality: 90173 distinct values High cardinality
order_delivered_carrier_date has a high cardinality: 81016 distinct values High cardinality
order_delivered_customer_date has a high cardinality: 95663 distinct values High cardinality
order_estimated_delivery_date has a high cardinality: 449 distinct values High cardinality
customer_unique_id has a high cardinality: 95419 distinct values High cardinality
customer_city has a high cardinality: 4110 distinct values High cardinality
price is highly correlated with payment_valueHigh correlation
freight_value is highly correlated with product_weight_gHigh correlation
payment_value is highly correlated with priceHigh correlation
product_weight_g is highly correlated with freight_value and 2 other fieldsHigh correlation
product_length_cm is highly correlated with product_width_cmHigh correlation
product_height_cm is highly correlated with product_weight_gHigh correlation
product_width_cm is highly correlated with product_weight_g and 1 other fieldsHigh correlation
price is highly correlated with payment_value and 1 other fieldsHigh correlation
payment_value is highly correlated with priceHigh correlation
product_weight_g is highly correlated with price and 3 other fieldsHigh correlation
product_length_cm is highly correlated with product_weight_g and 1 other fieldsHigh correlation
product_height_cm is highly correlated with product_weight_gHigh correlation
product_width_cm is highly correlated with product_weight_g and 1 other fieldsHigh correlation
order_item_id is highly correlated with payment_sequentialHigh correlation
price is highly correlated with payment_sequential and 1 other fieldsHigh correlation
freight_value is highly correlated with payment_sequentialHigh correlation
payment_sequential is highly correlated with order_item_id and 2 other fieldsHigh correlation
payment_value is highly correlated with priceHigh correlation
product_height_cm is highly correlated with product_category_nameHigh correlation
product_description_lenght is highly correlated with product_category_nameHigh correlation
price is highly correlated with payment_valueHigh correlation
customer_state is highly correlated with customer_zip_code_prefixHigh correlation
product_length_cm is highly correlated with product_width_cm and 1 other fieldsHigh correlation
product_weight_g is highly correlated with product_category_nameHigh correlation
product_width_cm is highly correlated with product_length_cm and 1 other fieldsHigh correlation
seller_state is highly correlated with seller_zip_code_prefix and 1 other fieldsHigh correlation
customer_zip_code_prefix is highly correlated with customer_stateHigh correlation
seller_zip_code_prefix is highly correlated with seller_state and 1 other fieldsHigh correlation
product_category_name is highly correlated with product_height_cm and 6 other fieldsHigh correlation
payment_value is highly correlated with priceHigh correlation
product_category_name has 1709 (1.4%) missing values Missing
product_name_lenght has 1709 (1.4%) missing values Missing
product_description_lenght has 1709 (1.4%) missing values Missing
product_photos_qty has 1709 (1.4%) missing values Missing
review_comment_title has 104226 (88.1%) missing values Missing
review_comment_message has 67650 (57.2%) missing values Missing
order_delivered_carrier_date has 1254 (1.1%) missing values Missing
order_delivered_customer_date has 2588 (2.2%) missing values Missing
order_id is uniformly distributed Uniform
shipping_limit_date is uniformly distributed Uniform
review_id is uniformly distributed Uniform
review_answer_timestamp is uniformly distributed Uniform
customer_id is uniformly distributed Uniform
order_purchase_timestamp is uniformly distributed Uniform
order_approved_at is uniformly distributed Uniform
order_delivered_customer_date is uniformly distributed Uniform
customer_unique_id is uniformly distributed Uniform

Reproduction

Analysis started2021-08-09 19:56:27.245288
Analysis finished2021-08-09 21:15:56.937797
Duration1 hour, 19 minutes and 29.69 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

order_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct98665
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Memory size10.9 MiB
895ab968e7bb0d5659d16cd74cd1650c
 
63
fedcd9f7ccdc8cba3a18defedd1a5547
 
38
fa65dad1b0e818e3ccc5cb0e39231352
 
29
ccf804e764ed5650cd8759557269dc13
 
26
6d58638e32674bebee793a47ac4cbadc
 
24
Other values (98660)
118135 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3786080
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85739 ?
Unique (%)72.5%

Sample

1st row00010242fe8c5a6d1ba2dd792cb16214
2nd row130898c0987d1801452a8ed92a670612
3rd row532ed5e14e24ae1f0d735b91524b98b9
4th row6f8c31653edb8c83e1a739408b5ff750
5th row7d19f4ef4d04461989632411b7e588b9

Common Values

ValueCountFrequency (%)
895ab968e7bb0d5659d16cd74cd1650c63
 
0.1%
fedcd9f7ccdc8cba3a18defedd1a554738
 
< 0.1%
fa65dad1b0e818e3ccc5cb0e3923135229
 
< 0.1%
ccf804e764ed5650cd8759557269dc1326
 
< 0.1%
6d58638e32674bebee793a47ac4cbadc24
 
< 0.1%
68986e4324f6a21481df4e6e89abcf0124
 
< 0.1%
465c2e1bee4561cb39e0db8c5993aafc24
 
< 0.1%
c6492b842ac190db807c15aff21a7dd624
 
< 0.1%
a3725dfe487d359b5be08cac48b64ec524
 
< 0.1%
5a3b1c29a49756e75f1ef513383c0c1222
 
< 0.1%
Other values (98655)118017
99.7%

Length

2021-08-09T23:15:57.181054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
895ab968e7bb0d5659d16cd74cd1650c63
 
0.1%
fedcd9f7ccdc8cba3a18defedd1a554738
 
< 0.1%
fa65dad1b0e818e3ccc5cb0e3923135229
 
< 0.1%
ccf804e764ed5650cd8759557269dc1326
 
< 0.1%
6d58638e32674bebee793a47ac4cbadc24
 
< 0.1%
68986e4324f6a21481df4e6e89abcf0124
 
< 0.1%
465c2e1bee4561cb39e0db8c5993aafc24
 
< 0.1%
c6492b842ac190db807c15aff21a7dd624
 
< 0.1%
a3725dfe487d359b5be08cac48b64ec524
 
< 0.1%
5a3b1c29a49756e75f1ef513383c0c1222
 
< 0.1%
Other values (98655)118017
99.7%

Most occurring characters

ValueCountFrequency (%)
4237711
 
6.3%
6237574
 
6.3%
b237565
 
6.3%
e237315
 
6.3%
3236962
 
6.3%
c236920
 
6.3%
7236861
 
6.3%
8236857
 
6.3%
1236825
 
6.3%
a236543
 
6.2%
Other values (6)1414947
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2365838
62.5%
Lowercase Letter1420242
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4237711
10.0%
6237574
10.0%
3236962
10.0%
7236861
10.0%
8236857
10.0%
1236825
10.0%
2236349
10.0%
9236003
10.0%
0235394
9.9%
5235302
9.9%
Lowercase Letter
ValueCountFrequency (%)
b237565
16.7%
e237315
16.7%
c236920
16.7%
a236543
16.7%
f236195
16.6%
d235704
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common2365838
62.5%
Latin1420242
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4237711
10.0%
6237574
10.0%
3236962
10.0%
7236861
10.0%
8236857
10.0%
1236825
10.0%
2236349
10.0%
9236003
10.0%
0235394
9.9%
5235302
9.9%
Latin
ValueCountFrequency (%)
b237565
16.7%
e237315
16.7%
c236920
16.7%
a236543
16.7%
f236195
16.6%
d235704
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3786080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4237711
 
6.3%
6237574
 
6.3%
b237565
 
6.3%
e237315
 
6.3%
3236962
 
6.3%
c236920
 
6.3%
7236861
 
6.3%
8236857
 
6.3%
1236825
 
6.3%
a236543
 
6.2%
Other values (6)1414947
37.4%

order_item_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.196509318
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-08-09T23:15:57.277629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum21
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6994522013
Coefficient of variation (CV)0.5845773122
Kurtosis103.373708
Mean1.196509318
Median Absolute Deviation (MAD)0
Skewness7.552581474
Sum141565
Variance0.4892333819
MonotonicityNot monotonic
2021-08-09T23:15:57.369840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1103652
87.6%
210316
 
8.7%
32395
 
2.0%
4995
 
0.8%
5472
 
0.4%
6265
 
0.2%
761
 
0.1%
837
 
< 0.1%
929
 
< 0.1%
1026
 
< 0.1%
Other values (11)67
 
0.1%
ValueCountFrequency (%)
1103652
87.6%
210316
 
8.7%
32395
 
2.0%
4995
 
0.8%
5472
 
0.4%
6265
 
0.2%
761
 
0.1%
837
 
< 0.1%
929
 
< 0.1%
1026
 
< 0.1%
ValueCountFrequency (%)
211
 
< 0.1%
203
 
< 0.1%
193
 
< 0.1%
183
 
< 0.1%
173
 
< 0.1%
163
 
< 0.1%
155
 
< 0.1%
147
< 0.1%
138
< 0.1%
1213
< 0.1%

product_id
Categorical

HIGH CARDINALITY

Distinct32951
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Memory size10.9 MiB
aca2eb7d00ea1a7b8ebd4e68314663af
 
536
99a4788cb24856965c36a24e339b6058
 
528
422879e10f46682990de24d770e7f83d
 
508
389d119b48cf3043d311335e499d9c6b
 
406
368c6c730842d78016ad823897a372db
 
398
Other values (32946)
115939 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3786080
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17344 ?
Unique (%)14.7%

Sample

1st row4244733e06e7ecb4970a6e2683c13e61
2nd row4244733e06e7ecb4970a6e2683c13e61
3rd row4244733e06e7ecb4970a6e2683c13e61
4th row4244733e06e7ecb4970a6e2683c13e61
5th row4244733e06e7ecb4970a6e2683c13e61

Common Values

ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af536
 
0.5%
99a4788cb24856965c36a24e339b6058528
 
0.4%
422879e10f46682990de24d770e7f83d508
 
0.4%
389d119b48cf3043d311335e499d9c6b406
 
0.3%
368c6c730842d78016ad823897a372db398
 
0.3%
53759a2ecddad2bb87a079a1f1519f73391
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4357
 
0.3%
53b36df67ebb7c41585e8d54d6772e08327
 
0.3%
154e7e31ebfa092203795c972e5804a6295
 
0.2%
3dd2a17168ec895c781a9191c1e95ad7278
 
0.2%
Other values (32941)114291
96.6%

Length

2021-08-09T23:15:57.595308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af536
 
0.5%
99a4788cb24856965c36a24e339b6058528
 
0.4%
422879e10f46682990de24d770e7f83d508
 
0.4%
389d119b48cf3043d311335e499d9c6b406
 
0.3%
368c6c730842d78016ad823897a372db398
 
0.3%
53759a2ecddad2bb87a079a1f1519f73391
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4357
 
0.3%
53b36df67ebb7c41585e8d54d6772e08327
 
0.3%
154e7e31ebfa092203795c972e5804a6295
 
0.2%
3dd2a17168ec895c781a9191c1e95ad7278
 
0.2%
Other values (32941)114291
96.6%

Most occurring characters

ValueCountFrequency (%)
3243136
 
6.4%
9241101
 
6.4%
e238901
 
6.3%
8238256
 
6.3%
7238171
 
6.3%
4237494
 
6.3%
a237300
 
6.3%
c236404
 
6.2%
0236283
 
6.2%
2236127
 
6.2%
Other values (6)1402907
37.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2375922
62.8%
Lowercase Letter1410158
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3243136
10.2%
9241101
10.1%
8238256
10.0%
7238171
10.0%
4237494
10.0%
0236283
9.9%
2236127
9.9%
6235751
9.9%
5235621
9.9%
1233982
9.8%
Lowercase Letter
ValueCountFrequency (%)
e238901
16.9%
a237300
16.8%
c236404
16.8%
b235077
16.7%
d232766
16.5%
f229710
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common2375922
62.8%
Latin1410158
37.2%

Most frequent character per script

Common
ValueCountFrequency (%)
3243136
10.2%
9241101
10.1%
8238256
10.0%
7238171
10.0%
4237494
10.0%
0236283
9.9%
2236127
9.9%
6235751
9.9%
5235621
9.9%
1233982
9.8%
Latin
ValueCountFrequency (%)
e238901
16.9%
a237300
16.8%
c236404
16.8%
b235077
16.7%
d232766
16.5%
f229710
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3786080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3243136
 
6.4%
9241101
 
6.4%
e238901
 
6.3%
8238256
 
6.3%
7238171
 
6.3%
4237494
 
6.3%
a237300
 
6.3%
c236404
 
6.2%
0236283
 
6.2%
2236127
 
6.2%
Other values (6)1402907
37.1%

seller_id
Categorical

HIGH CARDINALITY

Distinct3095
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size10.9 MiB
4a3ca9315b744ce9f8e9374361493884
 
2155
6560211a19b47992c3666cc44a7e94c0
 
2130
1f50f920176fa81dab994f9023523100
 
2017
cc419e0650a3c5ba77189a1882b7556a
 
1893
da8622b14eb17ae2831f4ac5b9dab84a
 
1662
Other values (3090)
108458 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3786080
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique487 ?
Unique (%)0.4%

Sample

1st row48436dade18ac8b2bce089ec2a041202
2nd row48436dade18ac8b2bce089ec2a041202
3rd row48436dade18ac8b2bce089ec2a041202
4th row48436dade18ac8b2bce089ec2a041202
5th row48436dade18ac8b2bce089ec2a041202

Common Values

ValueCountFrequency (%)
4a3ca9315b744ce9f8e93743614938842155
 
1.8%
6560211a19b47992c3666cc44a7e94c02130
 
1.8%
1f50f920176fa81dab994f90235231002017
 
1.7%
cc419e0650a3c5ba77189a1882b7556a1893
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a1662
 
1.4%
955fee9216a65b617aa5c0531780ce601530
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa1477
 
1.2%
7c67e1448b00f6e969d365cea6b010ab1463
 
1.2%
7a67c85e85bb2ce8582c35f2203ad7361245
 
1.1%
ea8482cd71df3c1969d7b9473ff13abc1240
 
1.0%
Other values (3085)101503
85.8%

Length

2021-08-09T23:15:57.817892image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4a3ca9315b744ce9f8e93743614938842155
 
1.8%
6560211a19b47992c3666cc44a7e94c02130
 
1.8%
1f50f920176fa81dab994f90235231002017
 
1.7%
cc419e0650a3c5ba77189a1882b7556a1893
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a1662
 
1.4%
955fee9216a65b617aa5c0531780ce601530
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa1477
 
1.2%
7c67e1448b00f6e969d365cea6b010ab1463
 
1.2%
7a67c85e85bb2ce8582c35f2203ad7361245
 
1.1%
ea8482cd71df3c1969d7b9473ff13abc1240
 
1.0%
Other values (3085)101503
85.8%

Most occurring characters

ValueCountFrequency (%)
1256849
 
6.8%
c250008
 
6.6%
4248500
 
6.6%
6243523
 
6.4%
0242859
 
6.4%
a241366
 
6.4%
b240804
 
6.4%
3240746
 
6.4%
9235028
 
6.2%
2233712
 
6.2%
Other values (6)1352685
35.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2394716
63.3%
Lowercase Letter1391364
36.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1256849
10.7%
4248500
10.4%
6243523
10.2%
0242859
10.1%
3240746
10.1%
9235028
9.8%
2233712
9.8%
8231762
9.7%
5231061
9.6%
7230676
9.6%
Lowercase Letter
ValueCountFrequency (%)
c250008
18.0%
a241366
17.3%
b240804
17.3%
e222610
16.0%
f219175
15.8%
d217401
15.6%

Most occurring scripts

ValueCountFrequency (%)
Common2394716
63.3%
Latin1391364
36.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1256849
10.7%
4248500
10.4%
6243523
10.2%
0242859
10.1%
3240746
10.1%
9235028
9.8%
2233712
9.8%
8231762
9.7%
5231061
9.6%
7230676
9.6%
Latin
ValueCountFrequency (%)
c250008
18.0%
a241366
17.3%
b240804
17.3%
e222610
16.0%
f219175
15.8%
d217401
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3786080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1256849
 
6.8%
c250008
 
6.6%
4248500
 
6.6%
6243523
 
6.4%
0242859
 
6.4%
a241366
 
6.4%
b240804
 
6.4%
3240746
 
6.4%
9235028
 
6.2%
2233712
 
6.2%
Other values (6)1352685
35.7%

shipping_limit_date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct93317
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Memory size9.5 MiB
2017-08-14 20:43:31
 
63
2017-10-05 17:44:41
 
38
2017-04-27 09:10:13
 
29
2017-06-15 16:15:08
 
26
2018-02-27 12:28:15
 
24
Other values (93312)
118135 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2247985
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76912 ?
Unique (%)65.0%

Sample

1st row2017-09-19 09:45:35
2nd row2017-07-05 02:44:11
3rd row2018-05-23 10:56:25
4th row2017-08-07 18:55:08
5th row2017-08-16 22:05:11

Common Values

ValueCountFrequency (%)
2017-08-14 20:43:3163
 
0.1%
2017-10-05 17:44:4138
 
< 0.1%
2017-04-27 09:10:1329
 
< 0.1%
2017-06-15 16:15:0826
 
< 0.1%
2018-02-27 12:28:1524
 
< 0.1%
2017-11-30 14:16:3424
 
< 0.1%
2017-03-15 23:39:2624
 
< 0.1%
2018-05-15 15:30:2824
 
< 0.1%
2017-07-13 15:10:1724
 
< 0.1%
2017-12-14 11:55:1722
 
< 0.1%
Other values (93307)118017
99.7%

Length

2021-08-09T23:15:58.041987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-301753
 
0.7%
2017-12-07794
 
0.3%
2018-04-19729
 
0.3%
2018-05-10696
 
0.3%
2018-01-18689
 
0.3%
2018-03-08681
 
0.3%
2018-08-07678
 
0.3%
2018-02-22673
 
0.3%
2018-03-22670
 
0.3%
2018-03-01663
 
0.3%
Other values (40674)228604
96.6%

Most occurring characters

ValueCountFrequency (%)
0379322
16.9%
1364451
16.2%
2289783
12.9%
-236630
10.5%
:236630
10.5%
8118675
 
5.3%
118315
 
5.3%
3114155
 
5.1%
5111990
 
5.0%
7102126
 
4.5%
Other values (3)175908
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1656410
73.7%
Dash Punctuation236630
 
10.5%
Other Punctuation236630
 
10.5%
Space Separator118315
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0379322
22.9%
1364451
22.0%
2289783
17.5%
8118675
 
7.2%
3114155
 
6.9%
5111990
 
6.8%
7102126
 
6.2%
479403
 
4.8%
649606
 
3.0%
946899
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
-236630
100.0%
Space Separator
ValueCountFrequency (%)
118315
100.0%
Other Punctuation
ValueCountFrequency (%)
:236630
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2247985
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0379322
16.9%
1364451
16.2%
2289783
12.9%
-236630
10.5%
:236630
10.5%
8118675
 
5.3%
118315
 
5.3%
3114155
 
5.1%
5111990
 
5.0%
7102126
 
4.5%
Other values (3)175908
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2247985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0379322
16.9%
1364451
16.2%
2289783
12.9%
-236630
10.5%
:236630
10.5%
8118675
 
5.3%
118315
 
5.3%
3114155
 
5.1%
5111990
 
5.0%
7102126
 
4.5%
Other values (3)175908
7.8%

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5968
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.651027
Minimum0.85
Maximum6735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-08-09T23:15:58.147854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile17
Q139.9
median74.9
Q3134.9
95-th percentile349.9
Maximum6735
Range6734.15
Interquartile range (IQR)95

Descriptive statistics

Standard deviation184.1096263
Coefficient of variation (CV)1.525968165
Kurtosis119.1492968
Mean120.651027
Median Absolute Deviation (MAD)42
Skewness7.892242206
Sum14274826.26
Variance33896.35449
MonotonicityNot monotonic
2021-08-09T23:15:58.264906image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.92619
 
2.2%
69.92113
 
1.8%
49.92051
 
1.7%
89.91644
 
1.4%
99.91526
 
1.3%
39.91403
 
1.2%
29.91387
 
1.2%
19.91284
 
1.1%
79.91282
 
1.1%
29.991228
 
1.0%
Other values (5958)101778
86.0%
ValueCountFrequency (%)
0.853
 
< 0.1%
1.220
< 0.1%
2.22
 
< 0.1%
2.291
 
< 0.1%
2.91
 
< 0.1%
2.991
 
< 0.1%
32
 
< 0.1%
3.063
 
< 0.1%
3.493
 
< 0.1%
3.57
 
< 0.1%
ValueCountFrequency (%)
67351
< 0.1%
67291
< 0.1%
64991
< 0.1%
47991
< 0.1%
46901
< 0.1%
45901
< 0.1%
4399.871
< 0.1%
4099.991
< 0.1%
40591
< 0.1%
3999.91
< 0.1%

freight_value
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct6999
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.03302362
Minimum0
Maximum409.68
Zeros390
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-08-09T23:15:58.388249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.78
Q113.08
median16.28
Q321.18
95-th percentile45.3
Maximum409.68
Range409.68
Interquartile range (IQR)8.1

Descriptive statistics

Standard deviation15.8365227
Coefficient of variation (CV)0.7905208419
Kurtosis57.63691305
Mean20.03302362
Median Absolute Deviation (MAD)3.63
Skewness5.543441345
Sum2370207.19
Variance250.7954513
MonotonicityNot monotonic
2021-08-09T23:15:58.504241image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.13861
 
3.3%
7.782355
 
2.0%
11.851999
 
1.7%
14.11992
 
1.7%
18.231632
 
1.4%
7.391573
 
1.3%
16.111211
 
1.0%
15.231064
 
0.9%
8.72970
 
0.8%
16.79930
 
0.8%
Other values (6989)100728
85.1%
ValueCountFrequency (%)
0390
0.3%
0.014
 
< 0.1%
0.023
 
< 0.1%
0.0314
 
< 0.1%
0.044
 
< 0.1%
0.059
 
< 0.1%
0.0613
 
< 0.1%
0.071
 
< 0.1%
0.0812
 
< 0.1%
0.096
 
< 0.1%
ValueCountFrequency (%)
409.681
< 0.1%
375.282
< 0.1%
339.591
< 0.1%
338.31
< 0.1%
322.11
< 0.1%
321.881
< 0.1%
321.461
< 0.1%
317.471
< 0.1%
314.41
< 0.1%
314.021
< 0.1%

payment_sequential
Real number (ℝ≥0)

HIGH CORRELATION

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.09406246
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-08-09T23:15:58.610078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum29
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.728555048
Coefficient of variation (CV)0.6659172345
Kurtosis346.9364476
Mean1.09406246
Median Absolute Deviation (MAD)0
Skewness15.89992468
Sum129444
Variance0.530792458
MonotonicityNot monotonic
2021-08-09T23:15:58.708784image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1113230
95.7%
23389
 
2.9%
3651
 
0.6%
4316
 
0.3%
5190
 
0.2%
6132
 
0.1%
791
 
0.1%
860
 
0.1%
949
 
< 0.1%
1041
 
< 0.1%
Other values (19)166
 
0.1%
ValueCountFrequency (%)
1113230
95.7%
23389
 
2.9%
3651
 
0.6%
4316
 
0.3%
5190
 
0.2%
6132
 
0.1%
791
 
0.1%
860
 
0.1%
949
 
< 0.1%
1041
 
< 0.1%
ValueCountFrequency (%)
291
 
< 0.1%
281
 
< 0.1%
271
 
< 0.1%
262
 
< 0.1%
252
 
< 0.1%
242
 
< 0.1%
232
 
< 0.1%
223
< 0.1%
216
< 0.1%
206
< 0.1%

payment_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
credit_card
87266 
boleto
23018 
voucher
 
6332
debit_card
 
1699

Length

Max length11
Median length11
Mean length9.79882517
Min length6

Characters and Unicode

Total characters1159348
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowboleto
3rd rowcredit_card
4th rowcredit_card
5th rowcredit_card

Common Values

ValueCountFrequency (%)
credit_card87266
73.8%
boleto23018
 
19.5%
voucher6332
 
5.4%
debit_card1699
 
1.4%

Length

2021-08-09T23:15:58.886376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T23:15:58.946105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
credit_card87266
73.8%
boleto23018
 
19.5%
voucher6332
 
5.4%
debit_card1699
 
1.4%

Most occurring characters

ValueCountFrequency (%)
c182563
15.7%
r182563
15.7%
d177930
15.3%
e118315
10.2%
t111983
9.7%
i88965
7.7%
_88965
7.7%
a88965
7.7%
o52368
 
4.5%
b24717
 
2.1%
Other values (4)42014
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1070383
92.3%
Connector Punctuation88965
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c182563
17.1%
r182563
17.1%
d177930
16.6%
e118315
11.1%
t111983
10.5%
i88965
8.3%
a88965
8.3%
o52368
 
4.9%
b24717
 
2.3%
l23018
 
2.2%
Other values (3)18996
 
1.8%
Connector Punctuation
ValueCountFrequency (%)
_88965
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1070383
92.3%
Common88965
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c182563
17.1%
r182563
17.1%
d177930
16.6%
e118315
11.1%
t111983
10.5%
i88965
8.3%
a88965
8.3%
o52368
 
4.9%
b24717
 
2.3%
l23018
 
2.2%
Other values (3)18996
 
1.8%
Common
ValueCountFrequency (%)
_88965
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1159348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c182563
15.7%
r182563
15.7%
d177930
15.3%
e118315
10.2%
t111983
9.7%
i88965
7.7%
_88965
7.7%
a88965
7.7%
o52368
 
4.5%
b24717
 
2.1%
Other values (4)42014
 
3.6%

payment_installments
Real number (ℝ≥0)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.943498288
Minimum0
Maximum24
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-08-09T23:15:59.016843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.778022129
Coefficient of variation (CV)0.943782485
Kurtosis2.506924009
Mean2.943498288
Median Absolute Deviation (MAD)1
Skewness1.618521535
Sum348260
Variance7.717406951
MonotonicityNot monotonic
2021-08-09T23:15:59.114324image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
158945
49.8%
213768
 
11.6%
311831
 
10.0%
48025
 
6.8%
106923
 
5.9%
56058
 
5.1%
85108
 
4.3%
64653
 
3.9%
71837
 
1.6%
9730
 
0.6%
Other values (14)437
 
0.4%
ValueCountFrequency (%)
03
 
< 0.1%
158945
49.8%
213768
 
11.6%
311831
 
10.0%
48025
 
6.8%
56058
 
5.1%
64653
 
3.9%
71837
 
1.6%
85108
 
4.3%
9730
 
0.6%
ValueCountFrequency (%)
2434
 
< 0.1%
231
 
< 0.1%
221
 
< 0.1%
216
 
< 0.1%
2021
 
< 0.1%
1838
< 0.1%
177
 
< 0.1%
167
 
< 0.1%
1593
0.1%
1416
 
< 0.1%

payment_value
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct28938
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.575651
Minimum0
Maximum13664.08
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-08-09T23:15:59.219652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.19
Q160.85
median108.2
Q3189.26
95-th percentile514.766
Maximum13664.08
Range13664.08
Interquartile range (IQR)128.41

Descriptive statistics

Standard deviation267.1046609
Coefficient of variation (CV)1.547754039
Kurtosis508.217825
Mean172.575651
Median Absolute Deviation (MAD)56.64
Skewness14.08641401
Sum20418288.15
Variance71344.8999
MonotonicityNot monotonic
2021-08-09T23:15:59.335780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50350
 
0.3%
100299
 
0.3%
20285
 
0.2%
77.57250
 
0.2%
35163
 
0.1%
73.34159
 
0.1%
30136
 
0.1%
116.94132
 
0.1%
56.78122
 
0.1%
107.78119
 
0.1%
Other values (28928)116300
98.3%
ValueCountFrequency (%)
06
< 0.1%
0.016
< 0.1%
0.032
 
< 0.1%
0.052
 
< 0.1%
0.082
 
< 0.1%
0.091
 
< 0.1%
0.13
< 0.1%
0.112
 
< 0.1%
0.132
 
< 0.1%
0.145
< 0.1%
ValueCountFrequency (%)
13664.088
< 0.1%
7274.884
< 0.1%
6929.311
 
< 0.1%
6922.211
 
< 0.1%
6726.661
 
< 0.1%
6081.546
< 0.1%
4950.341
 
< 0.1%
4809.442
 
< 0.1%
4764.341
 
< 0.1%
4681.781
 
< 0.1%

product_category_name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct73
Distinct (%)0.1%
Missing1709
Missing (%)1.4%
Memory size8.9 MiB
cama_mesa_banho
11990 
beleza_saude
10030 
esporte_lazer
9005 
moveis_decoracao
8833 
informatica_acessorios
8151 
Other values (68)
68597 

Length

Max length46
Median length15
Mean length14.87630139
Min length3

Characters and Unicode

Total characters1734666
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcool_stuff
2nd rowcool_stuff
3rd rowcool_stuff
4th rowcool_stuff
5th rowcool_stuff

Common Values

ValueCountFrequency (%)
cama_mesa_banho11990
 
10.1%
beleza_saude10030
 
8.5%
esporte_lazer9005
 
7.6%
moveis_decoracao8833
 
7.5%
informatica_acessorios8151
 
6.9%
utilidades_domesticas7380
 
6.2%
relogios_presentes6213
 
5.3%
telefonia4726
 
4.0%
ferramentas_jardim4590
 
3.9%
automotivo4400
 
3.7%
Other values (63)41288
34.9%

Length

2021-08-09T23:15:59.601226image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cama_mesa_banho11990
 
10.3%
beleza_saude10030
 
8.6%
esporte_lazer9005
 
7.7%
moveis_decoracao8833
 
7.6%
informatica_acessorios8151
 
7.0%
utilidades_domesticas7380
 
6.3%
relogios_presentes6213
 
5.3%
telefonia4726
 
4.1%
ferramentas_jardim4590
 
3.9%
automotivo4400
 
3.8%
Other values (63)41288
35.4%

Most occurring characters

ValueCountFrequency (%)
e210548
12.1%
a208844
12.0%
s172862
10.0%
o171626
9.9%
i115177
 
6.6%
r111462
 
6.4%
_110499
 
6.4%
t83293
 
4.8%
c82443
 
4.8%
m78690
 
4.5%
Other values (18)389222
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1623865
93.6%
Connector Punctuation110499
 
6.4%
Decimal Number302
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e210548
13.0%
a208844
12.9%
s172862
10.6%
o171626
10.6%
i115177
 
7.1%
r111462
 
6.9%
t83293
 
5.1%
c82443
 
5.1%
m78690
 
4.8%
n59162
 
3.6%
Other values (16)329758
20.3%
Connector Punctuation
ValueCountFrequency (%)
_110499
100.0%
Decimal Number
ValueCountFrequency (%)
2302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1623865
93.6%
Common110801
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e210548
13.0%
a208844
12.9%
s172862
10.6%
o171626
10.6%
i115177
 
7.1%
r111462
 
6.9%
t83293
 
5.1%
c82443
 
5.1%
m78690
 
4.8%
n59162
 
3.6%
Other values (16)329758
20.3%
Common
ValueCountFrequency (%)
_110499
99.7%
2302
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1734666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e210548
12.1%
a208844
12.0%
s172862
10.0%
o171626
9.9%
i115177
 
6.6%
r111462
 
6.4%
_110499
 
6.4%
t83293
 
4.8%
c82443
 
4.8%
m78690
 
4.5%
Other values (18)389222
22.4%

product_name_lenght
Real number (ℝ≥0)

MISSING

Distinct66
Distinct (%)0.1%
Missing1709
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean48.7677821
Minimum5
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-08-09T23:15:59.713794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile29
Q142
median52
Q357
95-th percentile60
Maximum76
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.03337512
Coefficient of variation (CV)0.2057377778
Kurtosis0.1496973763
Mean48.7677821
Median Absolute Deviation (MAD)6
Skewness-0.9049602992
Sum5686616
Variance100.6686163
MonotonicityNot monotonic
2021-08-09T23:15:59.833697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
598679
 
7.3%
608071
 
6.8%
566847
 
5.8%
586819
 
5.8%
576302
 
5.3%
555834
 
4.9%
545531
 
4.7%
534357
 
3.7%
524328
 
3.7%
493690
 
3.1%
Other values (56)56148
47.5%
ValueCountFrequency (%)
59
 
< 0.1%
63
 
< 0.1%
72
 
< 0.1%
84
 
< 0.1%
915
 
< 0.1%
109
 
< 0.1%
1111
 
< 0.1%
1238
< 0.1%
1326
< 0.1%
1447
< 0.1%
ValueCountFrequency (%)
761
 
< 0.1%
729
 
< 0.1%
691
 
< 0.1%
681
 
< 0.1%
673
 
< 0.1%
661
 
< 0.1%
64174
 
0.1%
631350
1.1%
62167
 
0.1%
61241
 
0.2%

product_description_lenght
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct2960
Distinct (%)2.5%
Missing1709
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean785.944008
Minimum4
Maximum3992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-08-09T23:15:59.959971image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile160
Q1346
median600
Q3983
95-th percentile2123
Maximum3992
Range3988
Interquartile range (IQR)637

Descriptive statistics

Standard deviation652.5786361
Coefficient of variation (CV)0.8303118662
Kurtosis4.930172973
Mean785.944008
Median Absolute Deviation (MAD)296
Skewness2.012211344
Sum91645787
Variance425858.8762
MonotonicityNot monotonic
2021-08-09T23:16:00.084068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
341711
 
0.6%
1893667
 
0.6%
348648
 
0.5%
903594
 
0.5%
492594
 
0.5%
245587
 
0.5%
366537
 
0.5%
236516
 
0.4%
340487
 
0.4%
919442
 
0.4%
Other values (2950)110823
93.7%
(Missing)1709
 
1.4%
ValueCountFrequency (%)
46
< 0.1%
82
 
< 0.1%
151
 
< 0.1%
207
< 0.1%
231
 
< 0.1%
262
 
< 0.1%
274
< 0.1%
282
 
< 0.1%
308
< 0.1%
312
 
< 0.1%
ValueCountFrequency (%)
39922
 
< 0.1%
39881
 
< 0.1%
39853
< 0.1%
39766
< 0.1%
39631
 
< 0.1%
39563
< 0.1%
39542
 
< 0.1%
39502
 
< 0.1%
39491
 
< 0.1%
39481
 
< 0.1%

product_photos_qty
Real number (ℝ≥0)

MISSING

Distinct19
Distinct (%)< 0.1%
Missing1709
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean2.205143818
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-08-09T23:16:00.191355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.717429086
Coefficient of variation (CV)0.7788286064
Kurtosis4.820243289
Mean2.205143818
Median Absolute Deviation (MAD)0
Skewness1.908767007
Sum257133
Variance2.949562667
MonotonicityNot monotonic
2021-08-09T23:16:00.280717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
158960
49.8%
223054
 
19.5%
312980
 
11.0%
48863
 
7.5%
55599
 
4.7%
63945
 
3.3%
71560
 
1.3%
8774
 
0.7%
10354
 
0.3%
9318
 
0.3%
Other values (9)199
 
0.2%
(Missing)1709
 
1.4%
ValueCountFrequency (%)
158960
49.8%
223054
 
19.5%
312980
 
11.0%
48863
 
7.5%
55599
 
4.7%
63945
 
3.3%
71560
 
1.3%
8774
 
0.7%
9318
 
0.3%
10354
 
0.3%
ValueCountFrequency (%)
201
 
< 0.1%
192
 
< 0.1%
184
 
< 0.1%
1711
 
< 0.1%
1512
 
< 0.1%
146
 
< 0.1%
1330
 
< 0.1%
1260
 
0.1%
1173
 
0.1%
10354
0.3%

product_weight_g
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2204
Distinct (%)1.9%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2112.331011
Minimum0
Maximum40425
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-08-09T23:16:00.395029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile125
Q1300
median700
Q31800
95-th percentile9850
Maximum40425
Range40425
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation3786.717861
Coefficient of variation (CV)1.792672569
Kurtosis16.0168339
Mean2112.331011
Median Absolute Deviation (MAD)500
Skewness3.582890275
Sum249878197
Variance14339232.16
MonotonicityNot monotonic
2021-08-09T23:16:00.520616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2007093
 
6.0%
1505411
 
4.6%
2504741
 
4.0%
3004430
 
3.7%
4003787
 
3.2%
1003666
 
3.1%
3503291
 
2.8%
5002856
 
2.4%
6002838
 
2.4%
7002149
 
1.8%
Other values (2194)78033
66.0%
ValueCountFrequency (%)
08
 
< 0.1%
25
 
< 0.1%
253
 
< 0.1%
50991
0.8%
532
 
< 0.1%
542
 
< 0.1%
552
 
< 0.1%
581
 
< 0.1%
609
 
< 0.1%
615
 
< 0.1%
ValueCountFrequency (%)
404253
 
< 0.1%
30000303
0.3%
298001
 
< 0.1%
297501
 
< 0.1%
297004
 
< 0.1%
296005
 
< 0.1%
295002
 
< 0.1%
292501
 
< 0.1%
291501
 
< 0.1%
291001
 
< 0.1%

product_length_cm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct99
Distinct (%)0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean30.26522676
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-08-09T23:16:00.648978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q118
median25
Q338
95-th percentile62
Maximum105
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.18903807
Coefficient of variation (CV)0.53490556
Kurtosis3.678790799
Mean30.26522676
Median Absolute Deviation (MAD)8
Skewness1.745703959
Sum3580225
Variance262.0849537
MonotonicityNot monotonic
2021-08-09T23:16:00.770448image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1618362
 
15.5%
2010999
 
9.3%
307952
 
6.7%
176203
 
5.2%
185909
 
5.0%
194898
 
4.1%
254871
 
4.1%
404360
 
3.7%
224000
 
3.4%
503163
 
2.7%
Other values (89)47578
40.2%
ValueCountFrequency (%)
732
 
< 0.1%
82
 
< 0.1%
94
 
< 0.1%
108
 
< 0.1%
1196
 
0.1%
1241
 
< 0.1%
1360
 
0.1%
14138
 
0.1%
15220
 
0.2%
1618362
15.5%
ValueCountFrequency (%)
105335
0.3%
10435
 
< 0.1%
10346
 
< 0.1%
10260
 
0.1%
101108
 
0.1%
100429
0.4%
9936
 
< 0.1%
9850
 
< 0.1%
9711
 
< 0.1%
968
 
< 0.1%

product_height_cm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct102
Distinct (%)0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean16.6200093
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-08-09T23:16:00.891005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q320
95-th percentile45
Maximum105
Range103
Interquartile range (IQR)12

Descriptive statistics

Standard deviation13.45394074
Coefficient of variation (CV)0.8095026001
Kurtosis7.276747824
Mean16.6200093
Median Absolute Deviation (MAD)6
Skewness2.238831941
Sum1966064
Variance181.0085215
MonotonicityNot monotonic
2021-08-09T23:16:01.015787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1010374
 
8.8%
206915
 
5.8%
156896
 
5.8%
126523
 
5.5%
116432
 
5.4%
25254
 
4.4%
44911
 
4.2%
84873
 
4.1%
54776
 
4.0%
164762
 
4.0%
Other values (92)56579
47.8%
ValueCountFrequency (%)
25254
4.4%
32821
 
2.4%
44911
4.2%
54776
4.0%
63576
 
3.0%
74387
3.7%
84873
4.1%
93408
 
2.9%
1010374
8.8%
116432
5.4%
ValueCountFrequency (%)
105139
0.1%
10414
 
< 0.1%
10349
 
< 0.1%
10210
 
< 0.1%
10043
 
< 0.1%
995
 
< 0.1%
983
 
< 0.1%
972
 
< 0.1%
968
 
< 0.1%
9522
 
< 0.1%

product_width_cm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct95
Distinct (%)0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean23.07507502
Minimum6
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-08-09T23:16:01.137083image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q115
median20
Q330
95-th percentile45
Maximum118
Range112
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.74931107
Coefficient of variation (CV)0.509177589
Kurtosis4.552246314
Mean23.07507502
Median Absolute Deviation (MAD)6
Skewness1.707015517
Sum2729666
Variance138.0463105
MonotonicityNot monotonic
2021-08-09T23:16:01.251033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2012701
 
10.7%
1111146
 
9.4%
159376
 
7.9%
168808
 
7.4%
308045
 
6.8%
125711
 
4.8%
135492
 
4.6%
144846
 
4.1%
184192
 
3.5%
404158
 
3.5%
Other values (85)43820
37.0%
ValueCountFrequency (%)
62
 
< 0.1%
75
 
< 0.1%
829
 
< 0.1%
951
 
< 0.1%
1083
 
0.1%
1111146
9.4%
125711
4.8%
135492
4.6%
144846
4.1%
159376
7.9%
ValueCountFrequency (%)
1188
 
< 0.1%
10514
 
< 0.1%
1041
 
< 0.1%
1031
 
< 0.1%
1022
 
< 0.1%
1012
 
< 0.1%
10043
< 0.1%
981
 
< 0.1%
971
 
< 0.1%
952
 
< 0.1%

seller_zip_code_prefix
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2246
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24440.7907
Minimum1001
Maximum99730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-08-09T23:16:01.371488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2972
Q16429
median13660
Q327946.5
95-th percentile88308
Maximum99730
Range98729
Interquartile range (IQR)21517.5

Descriptive statistics

Standard deviation27571.67946
Coefficient of variation (CV)1.12810096
Kurtosis0.9376893775
Mean24440.7907
Median Absolute Deviation (MAD)8123
Skewness1.556334563
Sum2891712152
Variance760197508
MonotonicityNot monotonic
2021-08-09T23:16:01.494752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149408375
 
7.1%
58492145
 
1.8%
150252098
 
1.8%
90151899
 
1.6%
134051678
 
1.4%
85771556
 
1.3%
47821549
 
1.3%
32041477
 
1.2%
41601268
 
1.1%
132321255
 
1.1%
Other values (2236)95015
80.3%
ValueCountFrequency (%)
100122
 
< 0.1%
102141
 
< 0.1%
10225
 
< 0.1%
10235
 
< 0.1%
1026323
0.3%
1031129
 
0.1%
103518
 
< 0.1%
10391
 
< 0.1%
104025
 
< 0.1%
10412
 
< 0.1%
ValueCountFrequency (%)
9973012
 
< 0.1%
997002
 
< 0.1%
996701
 
< 0.1%
9950061
0.1%
993002
 
< 0.1%
9897522
 
< 0.1%
989202
 
< 0.1%
9891014
 
< 0.1%
9880366
0.1%
987804
 
< 0.1%

seller_city
Categorical

HIGH CARDINALITY

Distinct611
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
sao paulo
29294 
ibitinga
8375 
curitiba
 
3158
santo andre
 
3149
sao jose do rio preto
 
2693
Other values (606)
71646 

Length

Max length40
Median length9
Mean length10.10233698
Min length2

Characters and Unicode

Total characters1195258
Distinct characters41
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)0.1%

Sample

1st rowvolta redonda
2nd rowvolta redonda
3rd rowvolta redonda
4th rowvolta redonda
5th rowvolta redonda

Common Values

ValueCountFrequency (%)
sao paulo29294
24.8%
ibitinga8375
 
7.1%
curitiba3158
 
2.7%
santo andre3149
 
2.7%
sao jose do rio preto2693
 
2.3%
belo horizonte2688
 
2.3%
rio de janeiro2535
 
2.1%
guarulhos2456
 
2.1%
ribeirao preto2374
 
2.0%
maringa2293
 
1.9%
Other values (601)59300
50.1%

Length

2021-08-09T23:16:01.759059image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sao36363
 
17.9%
paulo29575
 
14.5%
ibitinga8375
 
4.1%
rio5930
 
2.9%
do5524
 
2.7%
preto5518
 
2.7%
de4192
 
2.1%
jose4085
 
2.0%
santo3270
 
1.6%
andre3164
 
1.6%
Other values (640)97269
47.9%

Most occurring characters

ValueCountFrequency (%)
a198783
16.6%
o146217
12.2%
i102117
 
8.5%
85010
 
7.1%
r78219
 
6.5%
s76220
 
6.4%
e64172
 
5.4%
u62906
 
5.3%
p58420
 
4.9%
l56918
 
4.8%
Other values (31)266276
22.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1109029
92.8%
Space Separator85010
 
7.1%
Other Punctuation614
 
0.1%
Modifier Symbol369
 
< 0.1%
Dash Punctuation164
 
< 0.1%
Open Punctuation31
 
< 0.1%
Close Punctuation31
 
< 0.1%
Decimal Number8
 
< 0.1%
Nonspacing Mark2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a198783
17.9%
o146217
13.2%
i102117
9.2%
r78219
 
7.1%
s76220
 
6.9%
e64172
 
5.8%
u62906
 
5.7%
p58420
 
5.3%
l56918
 
5.1%
t47317
 
4.3%
Other values (14)217740
19.6%
Other Punctuation
ValueCountFrequency (%)
'347
56.5%
/141
23.0%
.76
 
12.4%
@38
 
6.2%
,6
 
1.0%
\6
 
1.0%
Decimal Number
ValueCountFrequency (%)
42
25.0%
22
25.0%
52
25.0%
01
12.5%
81
12.5%
Space Separator
ValueCountFrequency (%)
85010
100.0%
Modifier Symbol
ValueCountFrequency (%)
´369
100.0%
Dash Punctuation
ValueCountFrequency (%)
-164
100.0%
Open Punctuation
ValueCountFrequency (%)
(31
100.0%
Close Punctuation
ValueCountFrequency (%)
)31
100.0%
Nonspacing Mark
ValueCountFrequency (%)
̃2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1109029
92.8%
Common86227
 
7.2%
Inherited2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a198783
17.9%
o146217
13.2%
i102117
9.2%
r78219
 
7.1%
s76220
 
6.9%
e64172
 
5.8%
u62906
 
5.7%
p58420
 
5.3%
l56918
 
5.1%
t47317
 
4.3%
Other values (14)217740
19.6%
Common
ValueCountFrequency (%)
85010
98.6%
´369
 
0.4%
'347
 
0.4%
-164
 
0.2%
/141
 
0.2%
.76
 
0.1%
@38
 
< 0.1%
(31
 
< 0.1%
)31
 
< 0.1%
,6
 
< 0.1%
Other values (6)14
 
< 0.1%
Inherited
ValueCountFrequency (%)
̃2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1194887
> 99.9%
Latin 1 Sup369
 
< 0.1%
Diacriticals2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a198783
16.6%
o146217
12.2%
i102117
 
8.5%
85010
 
7.1%
r78219
 
6.5%
s76220
 
6.4%
e64172
 
5.4%
u62906
 
5.3%
p58420
 
4.9%
l56918
 
4.8%
Other values (29)265905
22.3%
Latin 1 Sup
ValueCountFrequency (%)
´369
100.0%
Diacriticals
ValueCountFrequency (%)
̃2
100.0%

seller_state
Categorical

HIGH CORRELATION

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
SP
84384 
MG
9314 
PR
9094 
RJ
 
5036
SC
 
4271
Other values (18)
 
6216

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters236630
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP84384
71.3%
MG9314
 
7.9%
PR9094
 
7.7%
RJ5036
 
4.3%
SC4271
 
3.6%
RS2294
 
1.9%
DF949
 
0.8%
BA700
 
0.6%
GO550
 
0.5%
PE465
 
0.4%
Other values (13)1258
 
1.1%

Length

2021-08-09T23:16:01.966531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp84384
71.3%
mg9314
 
7.9%
pr9094
 
7.7%
rj5036
 
4.3%
sc4271
 
3.6%
rs2294
 
1.9%
df949
 
0.8%
ba700
 
0.6%
go550
 
0.5%
pe465
 
0.4%
Other values (13)1258
 
1.1%

Most occurring characters

ValueCountFrequency (%)
P94007
39.7%
S91409
38.6%
R16494
 
7.0%
M9934
 
4.2%
G9864
 
4.2%
J5036
 
2.1%
C4375
 
1.8%
A1122
 
0.5%
E968
 
0.4%
D949
 
0.4%
Other values (6)2472
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter236630
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P94007
39.7%
S91409
38.6%
R16494
 
7.0%
M9934
 
4.2%
G9864
 
4.2%
J5036
 
2.1%
C4375
 
1.8%
A1122
 
0.5%
E968
 
0.4%
D949
 
0.4%
Other values (6)2472
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin236630
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P94007
39.7%
S91409
38.6%
R16494
 
7.0%
M9934
 
4.2%
G9864
 
4.2%
J5036
 
2.1%
C4375
 
1.8%
A1122
 
0.5%
E968
 
0.4%
D949
 
0.4%
Other values (6)2472
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII236630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P94007
39.7%
S91409
38.6%
R16494
 
7.0%
M9934
 
4.2%
G9864
 
4.2%
J5036
 
2.1%
C4375
 
1.8%
A1122
 
0.5%
E968
 
0.4%
D949
 
0.4%
Other values (6)2472
 
1.0%

review_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct98452
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Memory size10.9 MiB
eef5dbca8d37dfce6db7d7b16dd0525e
 
63
7145a6f0d38ec713897856cbdcfcdb7f
 
38
f28281373ab8815bafafe371218f02ce
 
29
8823bba1e3301fee652eb06de8ef9435
 
26
cc074f1c33940c4f0dd904705f98e39e
 
24
Other values (98447)
118135 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3786080
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85382 ?
Unique (%)72.2%

Sample

1st row97ca439bc427b48bc1cd7177abe71365
2nd rowb11cba360bbe71410c291b764753d37f
3rd rowaf01c4017c5ab46df6cc810e069e654a
4th row8304ff37d8b16b57086fa283fe0c44f8
5th row426f43a82185969503fb3c86241a9535

Common Values

ValueCountFrequency (%)
eef5dbca8d37dfce6db7d7b16dd0525e63
 
0.1%
7145a6f0d38ec713897856cbdcfcdb7f38
 
< 0.1%
f28281373ab8815bafafe371218f02ce29
 
< 0.1%
8823bba1e3301fee652eb06de8ef943526
 
< 0.1%
cc074f1c33940c4f0dd904705f98e39e24
 
< 0.1%
b5292206f96cd5d97359940203a0b51024
 
< 0.1%
b79b22bb50f78f1afe361661011fd89224
 
< 0.1%
b0c2f8c122ebef9f77753f7d167cf63424
 
< 0.1%
7e568736c98c553aea896a5dca746d5a22
 
< 0.1%
e8236fe7b6e1bdd513a500de361e2b8721
 
< 0.1%
Other values (98442)118020
99.8%

Length

2021-08-09T23:16:02.196893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
eef5dbca8d37dfce6db7d7b16dd0525e63
 
0.1%
7145a6f0d38ec713897856cbdcfcdb7f38
 
< 0.1%
f28281373ab8815bafafe371218f02ce29
 
< 0.1%
8823bba1e3301fee652eb06de8ef943526
 
< 0.1%
cc074f1c33940c4f0dd904705f98e39e24
 
< 0.1%
b5292206f96cd5d97359940203a0b51024
 
< 0.1%
b79b22bb50f78f1afe361661011fd89224
 
< 0.1%
b0c2f8c122ebef9f77753f7d167cf63424
 
< 0.1%
7e568736c98c553aea896a5dca746d5a22
 
< 0.1%
e8236fe7b6e1bdd513a500de361e2b8721
 
< 0.1%
Other values (98442)118020
99.8%

Most occurring characters

ValueCountFrequency (%)
a237429
 
6.3%
6237329
 
6.3%
5237083
 
6.3%
b237015
 
6.3%
d236979
 
6.3%
8236859
 
6.3%
f236814
 
6.3%
1236665
 
6.3%
0236561
 
6.2%
7236489
 
6.2%
Other values (6)1416857
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2365058
62.5%
Lowercase Letter1421022
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6237329
10.0%
5237083
10.0%
8236859
10.0%
1236665
10.0%
0236561
10.0%
7236489
10.0%
2236385
10.0%
9236079
10.0%
3235922
10.0%
4235686
10.0%
Lowercase Letter
ValueCountFrequency (%)
a237429
16.7%
b237015
16.7%
d236979
16.7%
f236814
16.7%
c236419
16.6%
e236366
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common2365058
62.5%
Latin1421022
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6237329
10.0%
5237083
10.0%
8236859
10.0%
1236665
10.0%
0236561
10.0%
7236489
10.0%
2236385
10.0%
9236079
10.0%
3235922
10.0%
4235686
10.0%
Latin
ValueCountFrequency (%)
a237429
16.7%
b237015
16.7%
d236979
16.7%
f236814
16.7%
c236419
16.6%
e236366
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3786080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a237429
 
6.3%
6237329
 
6.3%
5237083
 
6.3%
b237015
 
6.3%
d236979
 
6.3%
8236859
 
6.3%
f236814
 
6.3%
1236665
 
6.3%
0236561
 
6.2%
7236489
 
6.2%
Other values (6)1416857
37.4%

review_score
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
5
66370 
4
22352 
1
15427 
3
9965 
2
 
4201

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters118315
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
566370
56.1%
422352
 
18.9%
115427
 
13.0%
39965
 
8.4%
24201
 
3.6%

Length

2021-08-09T23:16:02.370044image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T23:16:02.433560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
566370
56.1%
422352
 
18.9%
115427
 
13.0%
39965
 
8.4%
24201
 
3.6%

Most occurring characters

ValueCountFrequency (%)
566370
56.1%
422352
 
18.9%
115427
 
13.0%
39965
 
8.4%
24201
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number118315
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
566370
56.1%
422352
 
18.9%
115427
 
13.0%
39965
 
8.4%
24201
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common118315
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
566370
56.1%
422352
 
18.9%
115427
 
13.0%
39965
 
8.4%
24201
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII118315
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
566370
56.1%
422352
 
18.9%
115427
 
13.0%
39965
 
8.4%
24201
 
3.6%

review_comment_title
Categorical

HIGH CARDINALITY
MISSING

Distinct4569
Distinct (%)32.4%
Missing104226
Missing (%)88.1%
Memory size5.1 MiB
Recomendo
 
498
recomendo
 
404
Bom
 
332
super recomendo
 
311
Excelente
 
293
Other values (4564)
12251 

Length

Max length26
Median length11
Mean length12.23379942
Min length1

Characters and Unicode

Total characters172362
Distinct characters126
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3120 ?
Unique (%)22.1%

Sample

1st rowsuper recomendo
2nd rowO produto não foi entregu
3rd rowMuito bom
4th rowMuito bom
5th rowqualidade ruim do produto

Common Values

ValueCountFrequency (%)
Recomendo498
 
0.4%
recomendo404
 
0.3%
Bom332
 
0.3%
super recomendo311
 
0.3%
Excelente293
 
0.2%
Muito bom279
 
0.2%
Ótimo268
 
0.2%
Super recomendo257
 
0.2%
Ótimo 238
 
0.2%
Otimo201
 
0.2%
Other values (4559)11008
 
9.3%
(Missing)104226
88.1%

Length

2021-08-09T23:16:03.056208image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
recomendo2485
 
9.2%
produto1589
 
5.9%
bom1528
 
5.7%
super1043
 
3.9%
muito1042
 
3.9%
não951
 
3.5%
ótimo807
 
3.0%
excelente769
 
2.8%
entrega728
 
2.7%
recebi450
 
1.7%
Other values (2112)15603
57.8%

Most occurring characters

ValueCountFrequency (%)
o21420
 
12.4%
e18455
 
10.7%
15344
 
8.9%
r10013
 
5.8%
t9486
 
5.5%
a9268
 
5.4%
m8473
 
4.9%
d8290
 
4.8%
i8146
 
4.7%
n7741
 
4.5%
Other values (116)55726
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter133893
77.7%
Uppercase Letter19045
 
11.0%
Space Separator15344
 
8.9%
Other Punctuation2703
 
1.6%
Decimal Number1270
 
0.7%
Other Symbol49
 
< 0.1%
Dash Punctuation19
 
< 0.1%
Modifier Symbol13
 
< 0.1%
Math Symbol8
 
< 0.1%
Close Punctuation7
 
< 0.1%
Other values (4)11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o21420
16.0%
e18455
13.8%
r10013
 
7.5%
t9486
 
7.1%
a9268
 
6.9%
m8473
 
6.3%
d8290
 
6.2%
i8146
 
6.1%
n7741
 
5.8%
c5992
 
4.5%
Other values (31)26609
19.9%
Uppercase Letter
ValueCountFrequency (%)
E2553
13.4%
R2065
10.8%
O1693
 
8.9%
P1632
 
8.6%
M1457
 
7.7%
N1214
 
6.4%
S1057
 
5.6%
A994
 
5.2%
Ó931
 
4.9%
B902
 
4.7%
Other values (26)4547
23.9%
Other Punctuation
ValueCountFrequency (%)
!1213
44.9%
.801
29.6%
*404
 
14.9%
,155
 
5.7%
?50
 
1.8%
/39
 
1.4%
%22
 
0.8%
:6
 
0.2%
;4
 
0.1%
"3
 
0.1%
Other values (4)6
 
0.2%
Other Symbol
ValueCountFrequency (%)
👍16
32.7%
😍9
18.4%
👏7
14.3%
🌟6
 
12.2%
💥5
 
10.2%
🚚1
 
2.0%
😀1
 
2.0%
🔟1
 
2.0%
👎1
 
2.0%
🤗1
 
2.0%
Decimal Number
ValueCountFrequency (%)
0484
38.1%
1424
33.4%
581
 
6.4%
280
 
6.3%
846
 
3.6%
342
 
3.3%
439
 
3.1%
929
 
2.3%
724
 
1.9%
621
 
1.7%
Modifier Symbol
ValueCountFrequency (%)
´6
46.2%
🏻3
23.1%
🏽2
 
15.4%
🏼2
 
15.4%
Close Punctuation
ValueCountFrequency (%)
)6
85.7%
]1
 
14.3%
Math Symbol
ValueCountFrequency (%)
+7
87.5%
=1
 
12.5%
Space Separator
ValueCountFrequency (%)
15344
100.0%
Dash Punctuation
ValueCountFrequency (%)
-19
100.0%
Currency Symbol
ValueCountFrequency (%)
$5
100.0%
Connector Punctuation
ValueCountFrequency (%)
_4
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Other Letter
ValueCountFrequency (%)
ª1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin152939
88.7%
Common19423
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o21420
14.0%
e18455
12.1%
r10013
 
6.5%
t9486
 
6.2%
a9268
 
6.1%
m8473
 
5.5%
d8290
 
5.4%
i8146
 
5.3%
n7741
 
5.1%
c5992
 
3.9%
Other values (68)45655
29.9%
Common
ValueCountFrequency (%)
15344
79.0%
!1213
 
6.2%
.801
 
4.1%
0484
 
2.5%
1424
 
2.2%
*404
 
2.1%
,155
 
0.8%
581
 
0.4%
280
 
0.4%
?50
 
0.3%
Other values (38)387
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII168670
97.9%
Latin 1 Sup3636
 
2.1%
None45
 
< 0.1%
Emoticons11
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o21420
12.7%
e18455
 
10.9%
15344
 
9.1%
r10013
 
5.9%
t9486
 
5.6%
a9268
 
5.5%
m8473
 
5.0%
d8290
 
4.9%
i8146
 
4.8%
n7741
 
4.6%
Other values (75)52034
30.8%
Latin 1 Sup
ValueCountFrequency (%)
ã1144
31.5%
Ó931
25.6%
á361
 
9.9%
ç358
 
9.8%
é275
 
7.6%
ó247
 
6.8%
Ã74
 
2.0%
í64
 
1.8%
ê45
 
1.2%
É30
 
0.8%
Other values (17)107
 
2.9%
Emoticons
ValueCountFrequency (%)
😍9
81.8%
😀1
 
9.1%
😤1
 
9.1%
None
ValueCountFrequency (%)
👍16
35.6%
👏7
15.6%
🌟6
 
13.3%
💥5
 
11.1%
🏻3
 
6.7%
🏽2
 
4.4%
🏼2
 
4.4%
🚚1
 
2.2%
🔟1
 
2.2%
👎1
 
2.2%

review_comment_message
Categorical

HIGH CARDINALITY
MISSING

Distinct36434
Distinct (%)71.9%
Missing67650
Missing (%)57.2%
Memory size9.9 MiB
Muito bom
 
259
Bom
 
207
muito bom
 
137
bom
 
118
Otimo
 
116
Other values (36429)
49828 

Length

Max length208
Median length55
Mean length71.77513076
Min length1

Characters and Unicode

Total characters3636487
Distinct characters212
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29741 ?
Unique (%)58.7%

Sample

1st rowPerfeito, produto entregue antes do combinado.
2nd rowlannister como sempre, entregou certinho e dentro do prazo. recomendo muito
3rd rowcarrinho muito bonito
4th rowMuito bom chegou bem antes do prazo, produto de ótima qualidade meu filho amou
5th rowo carrinho veio com defeito.

Common Values

ValueCountFrequency (%)
Muito bom259
 
0.2%
Bom207
 
0.2%
muito bom137
 
0.1%
bom118
 
0.1%
Otimo116
 
0.1%
Recomendo111
 
0.1%
otimo103
 
0.1%
Ok87
 
0.1%
Ótimo86
 
0.1%
Ótimo 85
 
0.1%
Other values (36424)49356
41.7%
(Missing)67650
57.2%

Length

2021-08-09T23:16:03.312727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
o23487
 
3.8%
produto21348
 
3.4%
e20473
 
3.3%
a15615
 
2.5%
de14952
 
2.4%
não13630
 
2.2%
do13273
 
2.1%
que10862
 
1.8%
prazo9452
 
1.5%
muito9205
 
1.5%
Other values (20373)468376
75.5%

Most occurring characters

ValueCountFrequency (%)
576384
15.9%
o357423
 
9.8%
e348604
 
9.6%
a287966
 
7.9%
r204653
 
5.6%
i167459
 
4.6%
t164244
 
4.5%
d153473
 
4.2%
n141236
 
3.9%
s136679
 
3.8%
Other values (202)1098366
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2740678
75.4%
Space Separator576384
 
15.9%
Uppercase Letter176403
 
4.9%
Other Punctuation98665
 
2.7%
Decimal Number25898
 
0.7%
Control14569
 
0.4%
Dash Punctuation1207
 
< 0.1%
Close Punctuation784
 
< 0.1%
Open Punctuation768
 
< 0.1%
Other Symbol718
 
< 0.1%
Other values (5)413
 
< 0.1%

Most frequent character per category

Other Symbol
ValueCountFrequency (%)
👏242
33.7%
👍86
 
12.0%
😍76
 
10.6%
°34
 
4.7%
😡24
 
3.3%
😉23
 
3.2%
😘20
 
2.8%
😆19
 
2.6%
👎15
 
2.1%
😁13
 
1.8%
Other values (57)166
23.1%
Lowercase Letter
ValueCountFrequency (%)
o357423
13.0%
e348604
12.7%
a287966
10.5%
r204653
 
7.5%
i167459
 
6.1%
t164244
 
6.0%
d153473
 
5.6%
n141236
 
5.2%
s136679
 
5.0%
m131074
 
4.8%
Other values (40)647867
23.6%
Uppercase Letter
ValueCountFrequency (%)
E20985
11.9%
O19705
11.2%
A18252
10.3%
P12919
 
7.3%
R12763
 
7.2%
C10218
 
5.8%
N10187
 
5.8%
M9741
 
5.5%
S8676
 
4.9%
T8257
 
4.7%
Other values (31)44700
25.3%
Other Punctuation
ValueCountFrequency (%)
.51762
52.5%
,28993
29.4%
!12925
 
13.1%
/1921
 
1.9%
?1653
 
1.7%
"434
 
0.4%
:385
 
0.4%
;226
 
0.2%
%196
 
0.2%
*82
 
0.1%
Other values (6)88
 
0.1%
Decimal Number
ValueCountFrequency (%)
05665
21.9%
15482
21.2%
24706
18.2%
32532
9.8%
41701
 
6.6%
51569
 
6.1%
61227
 
4.7%
81202
 
4.6%
71035
 
4.0%
9779
 
3.0%
Math Symbol
ValueCountFrequency (%)
+90
57.0%
=32
 
20.3%
|12
 
7.6%
<10
 
6.3%
~9
 
5.7%
×2
 
1.3%
>2
 
1.3%
÷1
 
0.6%
Modifier Symbol
ValueCountFrequency (%)
🏻34
33.3%
´26
25.5%
🏼15
14.7%
🏽13
 
12.7%
^8
 
7.8%
🏾4
 
3.9%
`2
 
2.0%
Control
ValueCountFrequency (%)
7277
49.9%
7271
49.9%
21
 
0.1%
Open Punctuation
ValueCountFrequency (%)
(762
99.2%
[6
 
0.8%
Close Punctuation
ValueCountFrequency (%)
)781
99.6%
]3
 
0.4%
Other Letter
ValueCountFrequency (%)
º31
59.6%
ª21
40.4%
Space Separator
ValueCountFrequency (%)
576384
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1207
100.0%
Connector Punctuation
ValueCountFrequency (%)
_14
100.0%
Currency Symbol
ValueCountFrequency (%)
$87
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2917133
80.2%
Common719354
 
19.8%

Most frequent character per script

Common
ValueCountFrequency (%)
576384
80.1%
.51762
 
7.2%
,28993
 
4.0%
!12925
 
1.8%
7277
 
1.0%
7271
 
1.0%
05665
 
0.8%
15482
 
0.8%
24706
 
0.7%
32532
 
0.4%
Other values (109)16357
 
2.3%
Latin
ValueCountFrequency (%)
o357423
12.3%
e348604
12.0%
a287966
 
9.9%
r204653
 
7.0%
i167459
 
5.7%
t164244
 
5.6%
d153473
 
5.3%
n141236
 
4.8%
s136679
 
4.7%
m131074
 
4.5%
Other values (83)824322
28.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3570160
98.2%
Latin 1 Sup65570
 
1.8%
None491
 
< 0.1%
Emoticons259
 
< 0.1%
Latin Ext A7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
576384
16.1%
o357423
 
10.0%
e348604
 
9.8%
a287966
 
8.1%
r204653
 
5.7%
i167459
 
4.7%
t164244
 
4.6%
d153473
 
4.3%
n141236
 
4.0%
s136679
 
3.8%
Other values (86)1032039
28.9%
Latin 1 Sup
ValueCountFrequency (%)
ã19563
29.8%
é11815
18.0%
á9368
14.3%
ç8039
12.3%
ó6440
 
9.8%
ê2027
 
3.1%
í1812
 
2.8%
Ó1569
 
2.4%
ú954
 
1.5%
õ945
 
1.4%
Other values (33)3038
 
4.6%
Emoticons
ValueCountFrequency (%)
😍76
29.3%
😡24
 
9.3%
😉23
 
8.9%
😘20
 
7.7%
😆19
 
7.3%
😁13
 
5.0%
😊12
 
4.6%
😀8
 
3.1%
😩7
 
2.7%
😃6
 
2.3%
Other values (22)51
19.7%
None
ValueCountFrequency (%)
👏242
49.3%
👍86
 
17.5%
🏻34
 
6.9%
👎15
 
3.1%
🏼15
 
3.1%
🏽13
 
2.6%
💓11
 
2.2%
🔝9
 
1.8%
🤗8
 
1.6%
🌟5
 
1.0%
Other values (28)53
 
10.8%
Latin Ext A
ValueCountFrequency (%)
ă5
71.4%
ķ1
 
14.3%
ő1
 
14.3%

review_creation_date
Categorical

HIGH CARDINALITY

Distinct633
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.5 MiB
2017-12-19 00:00:00
 
546
2018-05-22 00:00:00
 
523
2018-05-15 00:00:00
 
522
2017-12-20 00:00:00
 
517
2018-05-04 00:00:00
 
510
Other values (628)
115697 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2247985
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)< 0.1%

Sample

1st row2017-09-21 00:00:00
2nd row2017-07-14 00:00:00
3rd row2018-06-05 00:00:00
4th row2017-08-10 00:00:00
5th row2017-08-25 00:00:00

Common Values

ValueCountFrequency (%)
2017-12-19 00:00:00546
 
0.5%
2018-05-22 00:00:00523
 
0.4%
2018-05-15 00:00:00522
 
0.4%
2017-12-20 00:00:00517
 
0.4%
2018-05-04 00:00:00510
 
0.4%
2018-05-19 00:00:00509
 
0.4%
2018-08-28 00:00:00505
 
0.4%
2018-03-29 00:00:00504
 
0.4%
2018-04-12 00:00:00487
 
0.4%
2017-12-13 00:00:00486
 
0.4%
Other values (623)113206
95.7%

Length

2021-08-09T23:16:03.566888image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00118219
50.0%
2017-12-19546
 
0.2%
2018-05-22523
 
0.2%
2018-05-15522
 
0.2%
2017-12-20517
 
0.2%
2018-05-04510
 
0.2%
2018-05-19509
 
0.2%
2018-08-28505
 
0.2%
2018-03-29504
 
0.2%
2018-04-12487
 
0.2%
Other values (625)113788
48.1%

Most occurring characters

ValueCountFrequency (%)
0975204
43.4%
-236630
 
10.5%
:236630
 
10.5%
1205338
 
9.1%
2189022
 
8.4%
118315
 
5.3%
894573
 
4.2%
774311
 
3.3%
329091
 
1.3%
525342
 
1.1%
Other values (3)63529
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1656410
73.7%
Dash Punctuation236630
 
10.5%
Other Punctuation236630
 
10.5%
Space Separator118315
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0975204
58.9%
1205338
 
12.4%
2189022
 
11.4%
894573
 
5.7%
774311
 
4.5%
329091
 
1.8%
525342
 
1.5%
423775
 
1.4%
623455
 
1.4%
916299
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
-236630
100.0%
Space Separator
ValueCountFrequency (%)
118315
100.0%
Other Punctuation
ValueCountFrequency (%)
:236630
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2247985
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0975204
43.4%
-236630
 
10.5%
:236630
 
10.5%
1205338
 
9.1%
2189022
 
8.4%
118315
 
5.3%
894573
 
4.2%
774311
 
3.3%
329091
 
1.3%
525342
 
1.1%
Other values (3)63529
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2247985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0975204
43.4%
-236630
 
10.5%
:236630
 
10.5%
1205338
 
9.1%
2189022
 
8.4%
118315
 
5.3%
894573
 
4.2%
774311
 
3.3%
329091
 
1.3%
525342
 
1.1%
Other values (3)63529
 
2.8%

review_answer_timestamp
Categorical

HIGH CARDINALITY
UNIFORM

Distinct98290
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Memory size9.5 MiB
2017-08-17 22:17:55
 
63
2017-10-19 21:08:44
 
38
2017-05-24 16:21:27
 
29
2017-06-28 18:49:50
 
26
2017-03-23 08:34:13
 
24
Other values (98285)
118135 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2247985
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85104 ?
Unique (%)71.9%

Sample

1st row2017-09-22 10:57:03
2nd row2017-07-17 12:50:07
3rd row2018-06-06 21:41:12
4th row2017-08-13 03:35:17
5th row2017-08-28 00:51:18

Common Values

ValueCountFrequency (%)
2017-08-17 22:17:5563
 
0.1%
2017-10-19 21:08:4438
 
< 0.1%
2017-05-24 16:21:2729
 
< 0.1%
2017-06-28 18:49:5026
 
< 0.1%
2017-03-23 08:34:1324
 
< 0.1%
2017-08-01 13:20:3124
 
< 0.1%
2018-06-04 19:04:2024
 
< 0.1%
2018-03-07 15:08:1024
 
< 0.1%
2017-12-22 20:33:5222
 
< 0.1%
2017-07-30 14:19:0721
 
< 0.1%
Other values (98280)118020
99.8%

Length

2021-08-09T23:16:03.796542image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-05-20783
 
0.3%
2018-05-21691
 
0.3%
2018-05-10593
 
0.3%
2017-12-20477
 
0.2%
2018-04-13432
 
0.2%
2017-12-13426
 
0.2%
2018-05-11424
 
0.2%
2017-12-22417
 
0.2%
2018-08-24416
 
0.2%
2017-12-21411
 
0.2%
Other values (53976)231560
97.9%

Most occurring characters

ValueCountFrequency (%)
0378646
16.8%
1350776
15.6%
2299861
13.3%
-236630
10.5%
:236630
10.5%
8124472
 
5.5%
118315
 
5.3%
3109946
 
4.9%
7102187
 
4.5%
593806
 
4.2%
Other values (3)196716
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1656410
73.7%
Dash Punctuation236630
 
10.5%
Other Punctuation236630
 
10.5%
Space Separator118315
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0378646
22.9%
1350776
21.2%
2299861
18.1%
8124472
 
7.5%
3109946
 
6.6%
7102187
 
6.2%
593806
 
5.7%
493541
 
5.6%
653422
 
3.2%
949753
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
-236630
100.0%
Space Separator
ValueCountFrequency (%)
118315
100.0%
Other Punctuation
ValueCountFrequency (%)
:236630
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2247985
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0378646
16.8%
1350776
15.6%
2299861
13.3%
-236630
10.5%
:236630
10.5%
8124472
 
5.5%
118315
 
5.3%
3109946
 
4.9%
7102187
 
4.5%
593806
 
4.2%
Other values (3)196716
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2247985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0378646
16.8%
1350776
15.6%
2299861
13.3%
-236630
10.5%
:236630
10.5%
8124472
 
5.5%
118315
 
5.3%
3109946
 
4.9%
7102187
 
4.5%
593806
 
4.2%
Other values (3)196716
8.8%

customer_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct98665
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Memory size10.9 MiB
270c23a11d024a44c896d1894b261a83
 
63
13aa59158da63ba0e93ec6ac2c07aacb
 
38
9af2372a1e49340278e7c1ef8d749f34
 
29
92cd3ec6e2d643d4ebd0e3d6238f69e2
 
26
2ba91e12e5e4c9f56b82b86d9031d329
 
24
Other values (98660)
118135 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3786080
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85739 ?
Unique (%)72.5%

Sample

1st row3ce436f183e68e07877b285a838db11a
2nd rowe6eecc5a77de221464d1c4eaff0a9b64
3rd row4ef55bf80f711b372afebcb7c715344a
4th row30407a72ad8b3f4df4d15369126b20c9
5th row91a792fef70ecd8cc69d3c7feb3d12da

Common Values

ValueCountFrequency (%)
270c23a11d024a44c896d1894b261a8363
 
0.1%
13aa59158da63ba0e93ec6ac2c07aacb38
 
< 0.1%
9af2372a1e49340278e7c1ef8d749f3429
 
< 0.1%
92cd3ec6e2d643d4ebd0e3d6238f69e226
 
< 0.1%
2ba91e12e5e4c9f56b82b86d9031d32924
 
< 0.1%
86cc80fef09f7f39df4b0dbce48e81cb24
 
< 0.1%
63b964e79dee32a3587651701a2b8dbf24
 
< 0.1%
6ee2f17e3b6c33d6a9557f280edd292524
 
< 0.1%
d22f25a9fadfb1abbc2e29395b1239f424
 
< 0.1%
be1c4e52bb71e0c54b11a26b8e8d59f222
 
< 0.1%
Other values (98655)118017
99.7%

Length

2021-08-09T23:16:04.025376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
270c23a11d024a44c896d1894b261a8363
 
0.1%
13aa59158da63ba0e93ec6ac2c07aacb38
 
< 0.1%
9af2372a1e49340278e7c1ef8d749f3429
 
< 0.1%
92cd3ec6e2d643d4ebd0e3d6238f69e226
 
< 0.1%
2ba91e12e5e4c9f56b82b86d9031d32924
 
< 0.1%
86cc80fef09f7f39df4b0dbce48e81cb24
 
< 0.1%
63b964e79dee32a3587651701a2b8dbf24
 
< 0.1%
6ee2f17e3b6c33d6a9557f280edd292524
 
< 0.1%
d22f25a9fadfb1abbc2e29395b1239f424
 
< 0.1%
be1c4e52bb71e0c54b11a26b8e8d59f222
 
< 0.1%
Other values (98655)118017
99.7%

Most occurring characters

ValueCountFrequency (%)
f237304
 
6.3%
2237291
 
6.3%
5237079
 
6.3%
c236988
 
6.3%
6236914
 
6.3%
1236844
 
6.3%
8236669
 
6.3%
d236622
 
6.2%
e236620
 
6.2%
7236602
 
6.2%
Other values (6)1417147
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2365456
62.5%
Lowercase Letter1420624
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2237291
10.0%
5237079
10.0%
6236914
10.0%
1236844
10.0%
8236669
10.0%
7236602
10.0%
3236519
10.0%
9236477
10.0%
4235653
10.0%
0235408
10.0%
Lowercase Letter
ValueCountFrequency (%)
f237304
16.7%
c236988
16.7%
d236622
16.7%
e236620
16.7%
a236602
16.7%
b236488
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common2365456
62.5%
Latin1420624
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2237291
10.0%
5237079
10.0%
6236914
10.0%
1236844
10.0%
8236669
10.0%
7236602
10.0%
3236519
10.0%
9236477
10.0%
4235653
10.0%
0235408
10.0%
Latin
ValueCountFrequency (%)
f237304
16.7%
c236988
16.7%
d236622
16.7%
e236620
16.7%
a236602
16.7%
b236488
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3786080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f237304
 
6.3%
2237291
 
6.3%
5237079
 
6.3%
c236988
 
6.3%
6236914
 
6.3%
1236844
 
6.3%
8236669
 
6.3%
d236622
 
6.2%
e236620
 
6.2%
7236602
 
6.2%
Other values (6)1417147
37.4%

order_status
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.3 MiB
delivered
115728 
shipped
 
1255
canceled
 
570
invoiced
 
376
processing
 
376
Other values (2)
 
10

Length

Max length11
Median length9
Mean length8.97406077
Min length7

Characters and Unicode

Total characters1061766
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdelivered
2nd rowdelivered
3rd rowdelivered
4th rowdelivered
5th rowdelivered

Common Values

ValueCountFrequency (%)
delivered115728
97.8%
shipped1255
 
1.1%
canceled570
 
0.5%
invoiced376
 
0.3%
processing376
 
0.3%
unavailable7
 
< 0.1%
approved3
 
< 0.1%

Length

2021-08-09T23:16:04.212740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T23:16:04.285743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
delivered115728
97.8%
shipped1255
 
1.1%
canceled570
 
0.5%
invoiced376
 
0.3%
processing376
 
0.3%
unavailable7
 
< 0.1%
approved3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e350341
33.0%
d233660
22.0%
i118118
 
11.1%
l116312
 
11.0%
v116114
 
10.9%
r116107
 
10.9%
p2892
 
0.3%
s2007
 
0.2%
c1892
 
0.2%
n1329
 
0.1%
Other values (6)2994
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1061766
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e350341
33.0%
d233660
22.0%
i118118
 
11.1%
l116312
 
11.0%
v116114
 
10.9%
r116107
 
10.9%
p2892
 
0.3%
s2007
 
0.2%
c1892
 
0.2%
n1329
 
0.1%
Other values (6)2994
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin1061766
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e350341
33.0%
d233660
22.0%
i118118
 
11.1%
l116312
 
11.0%
v116114
 
10.9%
r116107
 
10.9%
p2892
 
0.3%
s2007
 
0.2%
c1892
 
0.2%
n1329
 
0.1%
Other values (6)2994
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1061766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e350341
33.0%
d233660
22.0%
i118118
 
11.1%
l116312
 
11.0%
v116114
 
10.9%
r116107
 
10.9%
p2892
 
0.3%
s2007
 
0.2%
c1892
 
0.2%
n1329
 
0.1%
Other values (6)2994
 
0.3%

order_purchase_timestamp
Categorical

HIGH CARDINALITY
UNIFORM

Distinct98111
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Memory size9.5 MiB
2017-08-08 20:26:31
 
63
2017-09-23 14:56:45
 
38
2017-04-20 12:45:34
 
29
2017-06-07 12:05:10
 
26
2018-02-14 16:34:27
 
24
Other values (98106)
118135 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2247985
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84913 ?
Unique (%)71.8%

Sample

1st row2017-09-13 08:59:02
2nd row2017-06-28 11:52:20
3rd row2018-05-18 10:25:53
4th row2017-08-01 18:38:42
5th row2017-08-10 21:48:40

Common Values

ValueCountFrequency (%)
2017-08-08 20:26:3163
 
0.1%
2017-09-23 14:56:4538
 
< 0.1%
2017-04-20 12:45:3429
 
< 0.1%
2017-06-07 12:05:1026
 
< 0.1%
2018-02-14 16:34:2724
 
< 0.1%
2018-05-12 12:28:5824
 
< 0.1%
2017-07-07 14:55:4324
 
< 0.1%
2017-11-25 13:54:3924
 
< 0.1%
2017-03-09 23:39:2624
 
< 0.1%
2017-10-17 13:06:2922
 
< 0.1%
Other values (98101)118017
99.7%

Length

2021-08-09T23:16:04.516587image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-241425
 
0.6%
2017-11-25639
 
0.3%
2017-11-27504
 
0.2%
2017-11-26482
 
0.2%
2017-11-28449
 
0.2%
2018-08-06445
 
0.2%
2018-05-15441
 
0.2%
2018-08-07440
 
0.2%
2018-05-07429
 
0.2%
2018-05-14425
 
0.2%
Other values (51248)230951
97.6%

Most occurring characters

ValueCountFrequency (%)
1365963
16.3%
0364100
16.2%
2288776
12.8%
-236630
10.5%
:236630
10.5%
8122682
 
5.5%
118315
 
5.3%
7110056
 
4.9%
3104525
 
4.6%
595595
 
4.3%
Other values (3)204713
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1656410
73.7%
Dash Punctuation236630
 
10.5%
Other Punctuation236630
 
10.5%
Space Separator118315
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1365963
22.1%
0364100
22.0%
2288776
17.4%
8122682
 
7.4%
7110056
 
6.6%
3104525
 
6.3%
595595
 
5.8%
495495
 
5.8%
656340
 
3.4%
952878
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
-236630
100.0%
Space Separator
ValueCountFrequency (%)
118315
100.0%
Other Punctuation
ValueCountFrequency (%)
:236630
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2247985
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1365963
16.3%
0364100
16.2%
2288776
12.8%
-236630
10.5%
:236630
10.5%
8122682
 
5.5%
118315
 
5.3%
7110056
 
4.9%
3104525
 
4.6%
595595
 
4.3%
Other values (3)204713
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2247985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1365963
16.3%
0364100
16.2%
2288776
12.8%
-236630
10.5%
:236630
10.5%
8122682
 
5.5%
118315
 
5.3%
7110056
 
4.9%
3104525
 
4.6%
595595
 
4.3%
Other values (3)204713
9.1%

order_approved_at
Categorical

HIGH CARDINALITY
UNIFORM

Distinct90173
Distinct (%)76.2%
Missing15
Missing (%)< 0.1%
Memory size9.5 MiB
2017-08-08 20:43:31
 
63
2017-09-25 17:44:41
 
38
2017-04-22 09:10:13
 
29
2017-06-09 16:15:08
 
26
2018-05-12 15:41:58
 
24
Other values (90168)
118120 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2247700
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72166 ?
Unique (%)61.0%

Sample

1st row2017-09-13 09:45:35
2nd row2017-06-29 02:44:11
3rd row2018-05-18 12:31:43
4th row2017-08-01 18:55:08
5th row2017-08-10 22:05:11

Common Values

ValueCountFrequency (%)
2017-08-08 20:43:3163
 
0.1%
2017-09-25 17:44:4138
 
< 0.1%
2017-04-22 09:10:1329
 
< 0.1%
2017-06-09 16:15:0826
 
< 0.1%
2018-05-12 15:41:5824
 
< 0.1%
2018-02-21 12:28:1524
 
< 0.1%
2017-03-09 23:39:2624
 
< 0.1%
2017-07-07 15:10:1724
 
< 0.1%
2017-11-25 14:16:3424
 
< 0.1%
2018-02-24 03:20:2723
 
< 0.1%
Other values (90163)118001
99.7%

Length

2021-08-09T23:16:04.752224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-04-241163
 
0.5%
2017-11-24992
 
0.4%
2017-11-25939
 
0.4%
2018-07-05825
 
0.3%
2017-11-28602
 
0.3%
2018-08-07505
 
0.2%
2018-05-08494
 
0.2%
2018-08-20488
 
0.2%
2018-05-01487
 
0.2%
2018-01-22484
 
0.2%
Other values (42201)229621
97.1%

Most occurring characters

ValueCountFrequency (%)
0379084
16.9%
1364095
16.2%
2287685
12.8%
-236600
10.5%
:236600
10.5%
118300
 
5.3%
8116535
 
5.2%
5113898
 
5.1%
3110885
 
4.9%
7105027
 
4.7%
Other values (3)178991
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1656200
73.7%
Dash Punctuation236600
 
10.5%
Other Punctuation236600
 
10.5%
Space Separator118300
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0379084
22.9%
1364095
22.0%
2287685
17.4%
8116535
 
7.0%
5113898
 
6.9%
3110885
 
6.7%
7105027
 
6.3%
481998
 
5.0%
651103
 
3.1%
945890
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
-236600
100.0%
Space Separator
ValueCountFrequency (%)
118300
100.0%
Other Punctuation
ValueCountFrequency (%)
:236600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2247700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0379084
16.9%
1364095
16.2%
2287685
12.8%
-236600
10.5%
:236600
10.5%
118300
 
5.3%
8116535
 
5.2%
5113898
 
5.1%
3110885
 
4.9%
7105027
 
4.7%
Other values (3)178991
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2247700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0379084
16.9%
1364095
16.2%
2287685
12.8%
-236600
10.5%
:236600
10.5%
118300
 
5.3%
8116535
 
5.2%
5113898
 
5.1%
3110885
 
4.9%
7105027
 
4.7%
Other values (3)178991
8.0%

order_delivered_carrier_date
Categorical

HIGH CARDINALITY
MISSING

Distinct81016
Distinct (%)69.2%
Missing1254
Missing (%)1.1%
Memory size9.4 MiB
2017-08-10 11:58:14
 
63
2018-05-09 15:48:00
 
48
2017-10-02 23:47:54
 
38
2018-05-10 18:29:00
 
36
2018-05-14 14:25:00
 
32
Other values (81011)
116844 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2224159
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61374 ?
Unique (%)52.4%

Sample

1st row2017-09-19 18:34:16
2nd row2017-07-05 12:00:33
3rd row2018-05-23 14:05:00
4th row2017-08-02 19:07:36
5th row2017-08-11 19:43:07

Common Values

ValueCountFrequency (%)
2017-08-10 11:58:1463
 
0.1%
2018-05-09 15:48:0048
 
< 0.1%
2017-10-02 23:47:5438
 
< 0.1%
2018-05-10 18:29:0036
 
< 0.1%
2018-05-14 14:25:0032
 
< 0.1%
2018-05-04 15:46:0029
 
< 0.1%
2017-04-24 11:31:1729
 
< 0.1%
2018-08-08 15:01:0027
 
< 0.1%
2017-06-16 15:50:2826
 
< 0.1%
2018-06-13 14:13:0024
 
< 0.1%
Other values (81006)116709
98.6%
(Missing)1254
 
1.1%

Length

2021-08-09T23:16:04.966656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-28925
 
0.4%
2017-11-27818
 
0.3%
2017-11-29706
 
0.3%
2018-02-27632
 
0.3%
2018-03-27622
 
0.3%
2018-08-06595
 
0.3%
2017-11-30584
 
0.2%
2018-05-14568
 
0.2%
2018-08-13550
 
0.2%
2018-05-03539
 
0.2%
Other values (37538)227583
97.2%

Most occurring characters

ValueCountFrequency (%)
0405314
18.2%
1346875
15.6%
2275775
12.4%
-234122
10.5%
:234122
10.5%
8123414
 
5.5%
117061
 
5.3%
7106645
 
4.8%
398644
 
4.4%
492073
 
4.1%
Other values (3)190114
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1638854
73.7%
Dash Punctuation234122
 
10.5%
Other Punctuation234122
 
10.5%
Space Separator117061
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0405314
24.7%
1346875
21.2%
2275775
16.8%
8123414
 
7.5%
7106645
 
6.5%
398644
 
6.0%
492073
 
5.6%
589896
 
5.5%
651438
 
3.1%
948780
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
-234122
100.0%
Space Separator
ValueCountFrequency (%)
117061
100.0%
Other Punctuation
ValueCountFrequency (%)
:234122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2224159
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0405314
18.2%
1346875
15.6%
2275775
12.4%
-234122
10.5%
:234122
10.5%
8123414
 
5.5%
117061
 
5.3%
7106645
 
4.8%
398644
 
4.4%
492073
 
4.1%
Other values (3)190114
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2224159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0405314
18.2%
1346875
15.6%
2275775
12.4%
-234122
10.5%
:234122
10.5%
8123414
 
5.5%
117061
 
5.3%
7106645
 
4.8%
398644
 
4.4%
492073
 
4.1%
Other values (3)190114
8.5%

order_delivered_customer_date
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct95663
Distinct (%)82.7%
Missing2588
Missing (%)2.2%
Memory size9.4 MiB
2017-08-14 12:46:18
 
63
2017-10-18 22:35:50
 
38
2017-06-22 16:04:46
 
26
2018-06-01 15:18:45
 
24
2017-07-27 20:52:15
 
24
Other values (95658)
115552 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2198813
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82382 ?
Unique (%)71.2%

Sample

1st row2017-09-20 23:43:48
2nd row2017-07-13 20:39:29
3rd row2018-06-04 18:34:26
4th row2017-08-09 21:26:33
5th row2017-08-24 20:04:21

Common Values

ValueCountFrequency (%)
2017-08-14 12:46:1863
 
0.1%
2017-10-18 22:35:5038
 
< 0.1%
2017-06-22 16:04:4626
 
< 0.1%
2018-06-01 15:18:4524
 
< 0.1%
2017-07-27 20:52:1524
 
< 0.1%
2017-11-30 14:59:1824
 
< 0.1%
2018-02-28 20:09:1924
 
< 0.1%
2017-03-21 13:32:4524
 
< 0.1%
2017-10-22 14:43:5422
 
< 0.1%
2017-12-21 16:33:1022
 
< 0.1%
Other values (95653)115436
97.6%
(Missing)2588
 
2.2%

Length

2021-08-09T23:16:05.198916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-05-21529
 
0.2%
2018-05-14528
 
0.2%
2018-08-13507
 
0.2%
2018-05-18505
 
0.2%
2018-08-27504
 
0.2%
2018-05-03501
 
0.2%
2018-04-11495
 
0.2%
2017-12-11489
 
0.2%
2017-12-19484
 
0.2%
2018-04-30482
 
0.2%
Other values (41733)226430
97.8%

Most occurring characters

ValueCountFrequency (%)
1340135
15.5%
0336692
15.3%
2291234
13.2%
-231454
10.5%
:231454
10.5%
8135449
 
6.2%
115727
 
5.3%
7107269
 
4.9%
3107060
 
4.9%
4100138
 
4.6%
Other values (3)202201
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1620178
73.7%
Dash Punctuation231454
 
10.5%
Other Punctuation231454
 
10.5%
Space Separator115727
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1340135
21.0%
0336692
20.8%
2291234
18.0%
8135449
 
8.4%
7107269
 
6.6%
3107060
 
6.6%
4100138
 
6.2%
593760
 
5.8%
658490
 
3.6%
949951
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
-231454
100.0%
Space Separator
ValueCountFrequency (%)
115727
100.0%
Other Punctuation
ValueCountFrequency (%)
:231454
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2198813
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1340135
15.5%
0336692
15.3%
2291234
13.2%
-231454
10.5%
:231454
10.5%
8135449
 
6.2%
115727
 
5.3%
7107269
 
4.9%
3107060
 
4.9%
4100138
 
4.6%
Other values (3)202201
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2198813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1340135
15.5%
0336692
15.3%
2291234
13.2%
-231454
10.5%
:231454
10.5%
8135449
 
6.2%
115727
 
5.3%
7107269
 
4.9%
3107060
 
4.9%
4100138
 
4.6%
Other values (3)202201
9.2%

order_estimated_delivery_date
Categorical

HIGH CARDINALITY

Distinct449
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.5 MiB
2017-12-20 00:00:00
 
656
2018-03-12 00:00:00
 
616
2018-03-13 00:00:00
 
614
2018-05-29 00:00:00
 
609
2018-07-16 00:00:00
 
590
Other values (444)
115230 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2247985
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st row2017-09-29 00:00:00
2nd row2017-07-26 00:00:00
3rd row2018-06-07 00:00:00
4th row2017-08-25 00:00:00
5th row2017-09-01 00:00:00

Common Values

ValueCountFrequency (%)
2017-12-20 00:00:00656
 
0.6%
2018-03-12 00:00:00616
 
0.5%
2018-03-13 00:00:00614
 
0.5%
2018-05-29 00:00:00609
 
0.5%
2018-07-16 00:00:00590
 
0.5%
2018-07-05 00:00:00588
 
0.5%
2018-02-14 00:00:00587
 
0.5%
2018-05-28 00:00:00583
 
0.5%
2018-05-30 00:00:00582
 
0.5%
2017-12-19 00:00:00582
 
0.5%
Other values (439)112308
94.9%

Length

2021-08-09T23:16:05.421501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00118315
50.0%
2017-12-20656
 
0.3%
2018-03-12616
 
0.3%
2018-03-13614
 
0.3%
2018-05-29609
 
0.3%
2018-07-16590
 
0.2%
2018-07-05588
 
0.2%
2018-02-14587
 
0.2%
2018-05-28583
 
0.2%
2018-05-30582
 
0.2%
Other values (440)112890
47.7%

Most occurring characters

ValueCountFrequency (%)
0978611
43.5%
-236630
 
10.5%
:236630
 
10.5%
1202579
 
9.0%
2184497
 
8.2%
118315
 
5.3%
897763
 
4.3%
772165
 
3.2%
331712
 
1.4%
524344
 
1.1%
Other values (3)64739
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1656410
73.7%
Dash Punctuation236630
 
10.5%
Other Punctuation236630
 
10.5%
Space Separator118315
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0978611
59.1%
1202579
 
12.2%
2184497
 
11.1%
897763
 
5.9%
772165
 
4.4%
331712
 
1.9%
524344
 
1.5%
623060
 
1.4%
422101
 
1.3%
919578
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
-236630
100.0%
Space Separator
ValueCountFrequency (%)
118315
100.0%
Other Punctuation
ValueCountFrequency (%)
:236630
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2247985
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0978611
43.5%
-236630
 
10.5%
:236630
 
10.5%
1202579
 
9.0%
2184497
 
8.2%
118315
 
5.3%
897763
 
4.3%
772165
 
3.2%
331712
 
1.4%
524344
 
1.1%
Other values (3)64739
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2247985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0978611
43.5%
-236630
 
10.5%
:236630
 
10.5%
1202579
 
9.0%
2184497
 
8.2%
118315
 
5.3%
897763
 
4.3%
772165
 
3.2%
331712
 
1.4%
524344
 
1.1%
Other values (3)64739
 
2.9%

customer_unique_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct95419
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Memory size10.9 MiB
9a736b248f67d166d2fbb006bcb877c3
 
75
6fbc7cdadbb522125f4b27ae9dee4060
 
38
f9ae226291893fda10af7965268fb7f6
 
35
8af7ac63b2efbcbd88e5b11505e8098a
 
29
569aa12b73b5f7edeaa6f2a01603e381
 
26
Other values (95414)
118112 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3786080
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81018 ?
Unique (%)68.5%

Sample

1st row871766c5855e863f6eccc05f988b23cb
2nd row0fb8e3eab2d3e79d92bb3fffbb97f188
3rd row3419052c8c6b45daf79c1e426f9e9bcb
4th rowe7c828d22c0682c1565252deefbe334d
5th row0bb98ba72dcc08e95f9d8cc434e9a2cc

Common Values

ValueCountFrequency (%)
9a736b248f67d166d2fbb006bcb877c375
 
0.1%
6fbc7cdadbb522125f4b27ae9dee406038
 
< 0.1%
f9ae226291893fda10af7965268fb7f635
 
< 0.1%
8af7ac63b2efbcbd88e5b11505e8098a29
 
< 0.1%
569aa12b73b5f7edeaa6f2a01603e38126
 
< 0.1%
c8460e4251689ba205045f3ea17884a124
 
< 0.1%
90807fdb59eec2152bc977feeb6e47e724
 
< 0.1%
85963fd37bfd387aa6d915d8a106548624
 
< 0.1%
db1af3fd6b23ac3873ef02619d548f9c24
 
< 0.1%
1d2435aa3b858d45c707c9fc25e1877924
 
< 0.1%
Other values (95409)117992
99.7%

Length

2021-08-09T23:16:05.649800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9a736b248f67d166d2fbb006bcb877c375
 
0.1%
6fbc7cdadbb522125f4b27ae9dee406038
 
< 0.1%
f9ae226291893fda10af7965268fb7f635
 
< 0.1%
8af7ac63b2efbcbd88e5b11505e8098a29
 
< 0.1%
569aa12b73b5f7edeaa6f2a01603e38126
 
< 0.1%
c8460e4251689ba205045f3ea17884a124
 
< 0.1%
90807fdb59eec2152bc977feeb6e47e724
 
< 0.1%
85963fd37bfd387aa6d915d8a106548624
 
< 0.1%
db1af3fd6b23ac3873ef02619d548f9c24
 
< 0.1%
1d2435aa3b858d45c707c9fc25e1877924
 
< 0.1%
Other values (95409)117992
99.7%

Most occurring characters

ValueCountFrequency (%)
6237656
 
6.3%
b237239
 
6.3%
1237104
 
6.3%
a236995
 
6.3%
d236943
 
6.3%
8236741
 
6.3%
3236718
 
6.3%
e236647
 
6.3%
5236552
 
6.2%
2236547
 
6.2%
Other values (6)1416938
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2365944
62.5%
Lowercase Letter1420136
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6237656
10.0%
1237104
10.0%
8236741
10.0%
3236718
10.0%
5236552
10.0%
2236547
10.0%
9236461
10.0%
7236364
10.0%
0236193
10.0%
4235608
10.0%
Lowercase Letter
ValueCountFrequency (%)
b237239
16.7%
a236995
16.7%
d236943
16.7%
e236647
16.7%
f236430
16.6%
c235882
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common2365944
62.5%
Latin1420136
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6237656
10.0%
1237104
10.0%
8236741
10.0%
3236718
10.0%
5236552
10.0%
2236547
10.0%
9236461
10.0%
7236364
10.0%
0236193
10.0%
4235608
10.0%
Latin
ValueCountFrequency (%)
b237239
16.7%
a236995
16.7%
d236943
16.7%
e236647
16.7%
f236430
16.6%
c235882
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3786080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6237656
 
6.3%
b237239
 
6.3%
1237104
 
6.3%
a236995
 
6.3%
d236943
 
6.3%
8236741
 
6.3%
3236718
 
6.3%
e236647
 
6.3%
5236552
 
6.2%
2236547
 
6.2%
Other values (6)1416938
37.4%

customer_zip_code_prefix
Real number (ℝ≥0)

HIGH CORRELATION

Distinct14976
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35051.68158
Minimum1003
Maximum99990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-08-09T23:16:05.752661image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile3277.7
Q111310
median24310
Q358640
95-th percentile90582
Maximum99990
Range98987
Interquartile range (IQR)47330

Descriptive statistics

Standard deviation29824.81122
Coefficient of variation (CV)0.8508810385
Kurtosis-0.7823870696
Mean35051.68158
Median Absolute Deviation (MAD)16300
Skewness0.7845220785
Sum4147139706
Variance889519364.5
MonotonicityNot monotonic
2021-08-09T23:16:05.874397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24220161
 
0.1%
22790155
 
0.1%
22793154
 
0.1%
24230141
 
0.1%
22775128
 
0.1%
35162125
 
0.1%
29101119
 
0.1%
11740110
 
0.1%
13087108
 
0.1%
36570105
 
0.1%
Other values (14966)117009
98.9%
ValueCountFrequency (%)
10031
 
< 0.1%
10042
 
< 0.1%
10056
< 0.1%
10062
 
< 0.1%
10074
< 0.1%
10084
< 0.1%
10098
< 0.1%
10116
< 0.1%
10122
 
< 0.1%
10133
 
< 0.1%
ValueCountFrequency (%)
999901
 
< 0.1%
999803
 
< 0.1%
999701
 
< 0.1%
999652
 
< 0.1%
999601
 
< 0.1%
999553
 
< 0.1%
999509
< 0.1%
999402
 
< 0.1%
999305
< 0.1%
999251
 
< 0.1%

customer_city
Categorical

HIGH CARDINALITY

Distinct4110
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
sao paulo
18728 
rio de janeiro
 
8261
belo horizonte
 
3274
brasilia
 
2484
curitiba
 
1816
Other values (4105)
83752 

Length

Max length32
Median length9
Mean length10.33405739
Min length3

Characters and Unicode

Total characters1222674
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1033 ?
Unique (%)0.9%

Sample

1st rowcampos dos goytacazes
2nd rowjatai
3rd rowbelo horizonte
4th rowsao jose dos pinhais
5th rowconselheiro lafaiete

Common Values

ValueCountFrequency (%)
sao paulo18728
 
15.8%
rio de janeiro8261
 
7.0%
belo horizonte3274
 
2.8%
brasilia2484
 
2.1%
curitiba1816
 
1.5%
campinas1743
 
1.5%
porto alegre1667
 
1.4%
salvador1537
 
1.3%
guarulhos1404
 
1.2%
sao bernardo do campo1121
 
0.9%
Other values (4100)76280
64.5%

Length

2021-08-09T23:16:06.136571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sao25233
 
12.2%
paulo18809
 
9.1%
de11584
 
5.6%
rio9906
 
4.8%
janeiro8261
 
4.0%
do5067
 
2.4%
belo3348
 
1.6%
horizonte3302
 
1.6%
brasilia2494
 
1.2%
porto1984
 
1.0%
Other values (3280)117509
56.6%

Most occurring characters

ValueCountFrequency (%)
a201636
16.5%
o150915
12.3%
i93525
 
7.6%
r90617
 
7.4%
89182
 
7.3%
e79407
 
6.5%
s74861
 
6.1%
n54198
 
4.4%
u53650
 
4.4%
l53296
 
4.4%
Other values (21)281387
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1132940
92.7%
Space Separator89182
 
7.3%
Dash Punctuation288
 
< 0.1%
Other Punctuation262
 
< 0.1%
Decimal Number2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a201636
17.8%
o150915
13.3%
i93525
 
8.3%
r90617
 
8.0%
e79407
 
7.0%
s74861
 
6.6%
n54198
 
4.8%
u53650
 
4.7%
l53296
 
4.7%
p44441
 
3.9%
Other values (16)236394
20.9%
Decimal Number
ValueCountFrequency (%)
11
50.0%
41
50.0%
Space Separator
ValueCountFrequency (%)
89182
100.0%
Other Punctuation
ValueCountFrequency (%)
'262
100.0%
Dash Punctuation
ValueCountFrequency (%)
-288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1132940
92.7%
Common89734
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a201636
17.8%
o150915
13.3%
i93525
 
8.3%
r90617
 
8.0%
e79407
 
7.0%
s74861
 
6.6%
n54198
 
4.8%
u53650
 
4.7%
l53296
 
4.7%
p44441
 
3.9%
Other values (16)236394
20.9%
Common
ValueCountFrequency (%)
89182
99.4%
-288
 
0.3%
'262
 
0.3%
11
 
< 0.1%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1222674
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a201636
16.5%
o150915
12.3%
i93525
 
7.6%
r90617
 
7.4%
89182
 
7.3%
e79407
 
6.5%
s74861
 
6.1%
n54198
 
4.4%
u53650
 
4.4%
l53296
 
4.4%
Other values (21)281387
23.0%

customer_state
Categorical

HIGH CORRELATION

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
SP
49865 
RJ
15425 
MG
13718 
RS
6539 
PR
5988 
Other values (22)
26780 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters236630
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRJ
2nd rowGO
3rd rowMG
4th rowPR
5th rowMG

Common Values

ValueCountFrequency (%)
SP49865
42.1%
RJ15425
 
13.0%
MG13718
 
11.6%
RS6539
 
5.5%
PR5988
 
5.1%
SC4319
 
3.7%
BA4069
 
3.4%
DF2500
 
2.1%
GO2453
 
2.1%
ES2351
 
2.0%
Other values (17)11088
 
9.4%

Length

2021-08-09T23:16:06.347944image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp49865
42.1%
rj15425
 
13.0%
mg13718
 
11.6%
rs6539
 
5.5%
pr5988
 
5.1%
sc4319
 
3.7%
ba4069
 
3.4%
df2500
 
2.1%
go2453
 
2.1%
es2351
 
2.0%
Other values (17)11088
 
9.4%

Most occurring characters

ValueCountFrequency (%)
S64327
27.2%
P60178
25.4%
R28913
12.2%
M16723
 
7.1%
G16171
 
6.8%
J15425
 
6.5%
A6855
 
2.9%
E6207
 
2.6%
C5970
 
2.5%
B4709
 
2.0%
Other values (7)11152
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter236630
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S64327
27.2%
P60178
25.4%
R28913
12.2%
M16723
 
7.1%
G16171
 
6.8%
J15425
 
6.5%
A6855
 
2.9%
E6207
 
2.6%
C5970
 
2.5%
B4709
 
2.0%
Other values (7)11152
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin236630
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S64327
27.2%
P60178
25.4%
R28913
12.2%
M16723
 
7.1%
G16171
 
6.8%
J15425
 
6.5%
A6855
 
2.9%
E6207
 
2.6%
C5970
 
2.5%
B4709
 
2.0%
Other values (7)11152
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII236630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S64327
27.2%
P60178
25.4%
R28913
12.2%
M16723
 
7.1%
G16171
 
6.8%
J15425
 
6.5%
A6855
 
2.9%
E6207
 
2.6%
C5970
 
2.5%
B4709
 
2.0%
Other values (7)11152
 
4.7%

Interactions

2021-08-09T23:15:24.917366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:25.060598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:25.194339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:25.313881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:25.438425image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:25.558918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:25.683453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:25.804750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:25.939673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:26.070158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:26.315600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:26.437883image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:26.556013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:26.676332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:26.796530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:26.918679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:27.043548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:27.165833image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:27.282715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:27.405302image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:27.526362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:27.648872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:27.765149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:27.885972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:28.006802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:28.128192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:28.243019image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:28.359919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:28.475130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:28.592834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:28.711966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:28.822680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:28.932685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:29.037506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:29.147251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:29.253003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:29.365282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:29.474118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:29.704583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:29.818886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:29.931893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:30.037376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:30.142658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:30.249905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:30.358792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:30.466199image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:30.584021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:30.701881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:30.809951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:30.924207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:31.039192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:31.157259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:31.272390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:31.392483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:31.510630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:31.633172image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:31.745147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:31.857355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:31.970854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:32.085736image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:32.199708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:32.315245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:32.431238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:32.537799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:32.647667image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:32.757105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:32.874376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:32.988460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:33.106613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:33.221692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:33.345263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:33.459733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:33.570158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:33.832008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:33.948959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:34.061441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:34.181692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:34.301521image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:34.417311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:34.534610image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:34.648298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:34.768572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:34.885993image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:35.008867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:35.128758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:35.250487image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:35.363420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:35.478321image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:35.596771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:35.717849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:35.834577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:35.951856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:36.069991image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:36.180368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:36.294955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:36.407503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:36.523731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:36.638099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:36.757437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:36.875621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:36.997467image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:37.107581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:37.220295image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:37.332830image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:37.450776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:37.566115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:37.691777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:37.813956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:37.930805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:38.053988image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:38.172496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:38.295183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:38.418308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:38.544338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:38.667705image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:38.976018image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:39.098081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:39.217555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:39.339325image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:39.464560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:39.585669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:39.709266image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:39.828913image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:39.942922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:40.061707image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:40.176934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:40.297087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:40.416431image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:40.538004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:40.659159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:40.781345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:40.895370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:41.015414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:41.132100image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:41.251126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:41.371536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:41.496425image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:41.620806image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:41.738862image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:41.859650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:41.978490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:42.102726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:42.226152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:42.354025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:42.477705image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:42.602542image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:42.719969image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:42.838234image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:42.957473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:43.080939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:43.202246image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:43.313813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:43.425605image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:43.529397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:43.641286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:43.747193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:43.856640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:43.965024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:44.079759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:44.191881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:44.304103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:44.409763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:44.518805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:44.626440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:44.738850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:44.845895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:44.959202image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:45.311935image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-08-09T23:15:45.543491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:45.654809image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:45.768672image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:45.878770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:45.997033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:46.112500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:46.228159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:46.332762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:46.441319image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:46.550675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:46.664772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:46.774423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:46.888275image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:47.004287image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:47.112783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:47.225527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:47.335190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:47.449515image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:47.560939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:47.679640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:47.793649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:47.911000image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:48.019722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:48.128584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:48.237648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:48.350175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:48.463395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:48.579439image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:48.693933image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:48.799790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:48.911397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:49.023651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:49.141581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:49.254245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:49.375029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:49.495500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:49.612615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:49.721446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:49.831177image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:49.941006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:50.054911image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:50.165867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:50.282821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:50.400856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:50.512153image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:50.627588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:50.738846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:50.853816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:50.966216image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:51.085247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:51.202760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:51.320527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:51.429684image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:51.540232image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:51.651008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-09T23:15:51.764021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-08-09T23:16:06.450309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-09T23:16:06.657814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-09T23:16:06.859880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-09T23:16:07.073380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-09T23:16:07.275593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-09T23:15:52.513115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-09T23:15:54.007649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-09T23:15:55.719062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-09T23:15:56.244352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

order_idorder_item_idproduct_idseller_idshipping_limit_datepricefreight_valuepayment_sequentialpayment_typepayment_installmentspayment_valueproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmseller_zip_code_prefixseller_cityseller_statereview_idreview_scorereview_comment_titlereview_comment_messagereview_creation_datereview_answer_timestampcustomer_idorder_statusorder_purchase_timestamporder_approved_atorder_delivered_carrier_dateorder_delivered_customer_dateorder_estimated_delivery_datecustomer_unique_idcustomer_zip_code_prefixcustomer_citycustomer_state
000010242fe8c5a6d1ba2dd792cb1621414244733e06e7ecb4970a6e2683c13e6148436dade18ac8b2bce089ec2a0412022017-09-19 09:45:3558.913.291credit_card272.19cool_stuff58.0598.04.0650.028.09.014.027277volta redondaSP97ca439bc427b48bc1cd7177abe713655NaNPerfeito, produto entregue antes do combinado.2017-09-21 00:00:002017-09-22 10:57:033ce436f183e68e07877b285a838db11adelivered2017-09-13 08:59:022017-09-13 09:45:352017-09-19 18:34:162017-09-20 23:43:482017-09-29 00:00:00871766c5855e863f6eccc05f988b23cb28013campos dos goytacazesRJ
1130898c0987d1801452a8ed92a67061214244733e06e7ecb4970a6e2683c13e6148436dade18ac8b2bce089ec2a0412022017-07-05 02:44:1155.917.961boleto173.86cool_stuff58.0598.04.0650.028.09.014.027277volta redondaSPb11cba360bbe71410c291b764753d37f5NaNlannister como sempre, entregou certinho e dentro do prazo. recomendo muito2017-07-14 00:00:002017-07-17 12:50:07e6eecc5a77de221464d1c4eaff0a9b64delivered2017-06-28 11:52:202017-06-29 02:44:112017-07-05 12:00:332017-07-13 20:39:292017-07-26 00:00:000fb8e3eab2d3e79d92bb3fffbb97f18875800jataiGO
2532ed5e14e24ae1f0d735b91524b98b914244733e06e7ecb4970a6e2683c13e6148436dade18ac8b2bce089ec2a0412022018-05-23 10:56:2564.918.331credit_card283.23cool_stuff58.0598.04.0650.028.09.014.027277volta redondaSPaf01c4017c5ab46df6cc810e069e654a4super recomendocarrinho muito bonito2018-06-05 00:00:002018-06-06 21:41:124ef55bf80f711b372afebcb7c715344adelivered2018-05-18 10:25:532018-05-18 12:31:432018-05-23 14:05:002018-06-04 18:34:262018-06-07 00:00:003419052c8c6b45daf79c1e426f9e9bcb30720belo horizonteMG
36f8c31653edb8c83e1a739408b5ff75014244733e06e7ecb4970a6e2683c13e6148436dade18ac8b2bce089ec2a0412022017-08-07 18:55:0858.916.171credit_card375.07cool_stuff58.0598.04.0650.028.09.014.027277volta redondaSP8304ff37d8b16b57086fa283fe0c44f85NaNNaN2017-08-10 00:00:002017-08-13 03:35:1730407a72ad8b3f4df4d15369126b20c9delivered2017-08-01 18:38:422017-08-01 18:55:082017-08-02 19:07:362017-08-09 21:26:332017-08-25 00:00:00e7c828d22c0682c1565252deefbe334d83070sao jose dos pinhaisPR
47d19f4ef4d04461989632411b7e588b914244733e06e7ecb4970a6e2683c13e6148436dade18ac8b2bce089ec2a0412022017-08-16 22:05:1158.913.291credit_card472.19cool_stuff58.0598.04.0650.028.09.014.027277volta redondaSP426f43a82185969503fb3c86241a95355NaNNaN2017-08-25 00:00:002017-08-28 00:51:1891a792fef70ecd8cc69d3c7feb3d12dadelivered2017-08-10 21:48:402017-08-10 22:05:112017-08-11 19:43:072017-08-24 20:04:212017-09-01 00:00:000bb98ba72dcc08e95f9d8cc434e9a2cc36400conselheiro lafaieteMG
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Last rows

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